Machine Learning Models For Prediction Research Articles

Predicting Gold Prices Through Machine Learning with Economic Indicators and Feature Selection Methods

The enormous fluctuation of the gold price in international markets gives predictive analysis an indispensable place for business firms, economists, and investors. With this in mind, this research aims to construct a machine learning model for gold price prediction based on past information and identify some leading patterns affecting the prices of such a commodity. The model thus covers a range of related financial and economic factors, be it past gold prices and inflation metrics, oil price vagaries, worldwide market indexes, and interest rate movements. Using machine learning algorithm approaches like Support Vector Machine (SVM), Random Forest, as well as Linear Regression approaches, the system is set to identify complex relationships prevailing in the data and provides predictions for short-term movements and long-term movements concerning price. The model will be trained and validated using historical data that includes the history of the gold price. In this respect, the findings of the study are relevant to stakeholders in financial forecasting who could make informed investment decisions. Additionally, the paper has outlined the method for feature selection that determines which factors are dominant in explaining the fluctuation of the gold price. The goal of the model, therefore, is to enhance its performance through prediction accuracy reduction in noise, recursive elimination of features, and application of correlation analysis. As a result, a robust evaluation of several machine algorithms, including support vector machines, allows the selection of a suitable model that best solves the prediction task. This approach to forecasting will therefore aid in the creation of instruments for risk management, automated trading techniques, and financial strategies for an individual as well as for organizations. Keywords: Support Vector Machine, Random Forest, machine learning, and gold price prediction; economic indicators, feature selection, and linear regression in the field of financial forecasting; erratic behaviour; and risk management.

Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model

Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.

Optimized punching shear design in steel fiber-reinforced slabs: Machine learning vs. evolutionary prediction models

This research paper focuses on utilizing Artificial Neural Networks (ANN), Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR), and Gene Expression Programming (GEP) to predict the punching shear strength of Steel Fibre-Reinforced Concrete (SFRC) slabs.In order to formulate predictions, research and analysis were carried out making use of a dataset, this dataset included several parameters that impact on punching shear strength, including SFRC slabs longitudinally and transversely, using ANN, GEP, and MOGA-EPR methods. The developed models exhibited very good performance, as the soft computing techniques (GEP and MOGA-EPR) achieved R² values of 0.91 to 0.93, while the ANN technique was higher at 0.95. Furthermore, two case studies were incorporated to carry out cost analyses of the models in real-world applications. It was shown that the efficiency of the Machine Learning (ML) models in reducing the costs of materials is relatively high, as they were capable of better predictions than the standard methods employed by the codes.

Stiffness and hardness of thermally modified timber assessed with explainable machine learning

Thermal modification is an environmentally friendly practice to enhance the durability of wood. Despite the method's effectiveness in improving wood durability and dimensional stability, the mechanical properties of wood could be compromised following the heat treatment process. While most of the literature has focused on studying the impact of wood modification on stiffness or strength, less attention was given to characterization and nondestructive prediction of wood hardness following thermal treatment. Hardness, an important quality indicator of wood, has been a subject of limited research in the context of developing predictive models for assessing the hardness of thermally modified wood. The existing research in this area has either been scarce or unsuccessful, with poor predictive performance being a common issue. This research studies the impact of thermal modification on the longitudinal and transverse hardness of some common North American softwoods (Douglas-fir, Western hemlock, red cedar) and hardwoods (red alder, paper birch). It also aims to develop an explainable machine-learning (TreeNet gradient boosting machine) predictive model for hardness prediction using stress wave data combined with wood density and information about the type of wood species and heat treatment level. The impact of heat treatment on the stress wave velocity, density, and hardness was studied, and the dependency of the variables was assessed using correlation and clustering analysis. Machine learning modeling showed that the longitudinal and transverse hardness could be predicted by having a maximum R2 (test data) of 70.62 and 72.78, respectively, with a relative importance of the predictors in descending order as wood species > density > wave velocity >MOEdyn> heat treatment. Using the top 3 critical parameters yielded an R2 (test data) of ∼69 % for both models. The developed model could predict the hardness of various wood species treated at different temperatures, having only the type of wood species and its density followed by the stress wave velocity, which can be measured at an industrial scale.

Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data, RNA expression, and tumor microenvironment data, using fusion techniques to enhance model performance. We obtained data from the SEER database to assess the impact of nine demographic and clinical features on the survival of 58,495 GBM patients and built predictive machine learning models. Additionally, mRNA expression data from 600 GBM patients from TCGA, CGGA, and GEO were analyzed. We used Cox regression and LASSO to create a gene signature, which was compared against 13 published signatures for accuracy. Twenty-one machine learning models were applied to predict survival at multiple time points. Finally, we integrated multiple feature types using fusion techniques and developed a Shiny app to provide survival predictions for GBM patients. Using the SEER database, we constructed machine learning models based on nine clinical variables: age, gender, marital status, race, tumor site, laterality, surgery, chemotherapy, and radiation therapy. The best-performing model achieved AUC values of 0.775, 0.728, 0.692, and 0.683 for predicting survival at 6, 12, 18, and 24months in the testing cohort. In the merged TCGA, CGGA, and GEO cohorts, we identified 11 genes to develop predictive models. These 11 genes outperformed 13 other published gene signatures in predicting the prognosis of GBM. When incorporating mRNA features, tumor microenvironment features, and clinical variables into the fusion models, the AUC values for predicting survival at 6, 12, 18, and 24months were 0.641, 0.624, 0.655, and 0.637, respectively. A user-friendly tool for predicting the survival curve of individual GBM patients is available at https://zzubioinfo.shinyapps.io/mlGBM/ . Our study provides a web-based tool that includes two modules: one for predicting survival curves using only clinical variables, and another that integrates multiple feature types for more comprehensive predictions.

Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study

BackgroundNew-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML).MethodsThe data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions.ResultsAmong 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873–0.888) in validation and 0.769 (0.756–0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use.ConclusionWe developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.

Predicting Hass Avocado Maturity with NIR Spectroscopy for Non-invasive Dry Matter Estimation in Hass Avocados

Aims: To develop a rapid, non-invasive method for predicting Hass avocado maturity using near- infrared diffuse reflectance spectroscopy (NIR-DRS) combined with machine learning algorithms, and to identify the optimal NIR wavelength range for accurate dry matter content prediction. Study Design: An experimental design involving spectral data collection from Hass avocados and the development of machine learning models for dry matter prediction. Methodology: Spectral data from 200 Hass avocados were collected using near-infrared diffuse reflectance spectroscopy (900-2500 nm). To improve the quality of the spectral data and reduce noise, standard normal variate was used to correct for scattering and remove unwanted variability in the spectral data. PCA was then performed to reduce the dimension of the spectral data while retaining the most significant variance. Following preprocessing, machine learning models, including Convolutional Neural Networks (CNN), were trained to predict dry matter content, and the optimal wavelength range was determined for accurate prediction. Results: The CNN model demonstrated superior performance for dry matter prediction with R² of 0.91 in the testing set. The wavelength range of 1000-1500 nm was identified as optimal, offering accurate predictions while reducing computational complexity. This range shows potential for developing cost-effective NIR devices for real-time maturity assessment. Conclusion: NIR spectroscopy combined with machine learning offers a non-invasive, accurate method for predicting avocado dry matter, with potential applications for quality control in the avocado industry. The findings demonstrate that focusing on specific wavelength ranges can lead to more affordable and efficient NIR solutions.

Development of machine learning models for prediction of left ventricular systolic dysfunction in patients with isolated left ventricular dilation

Abstract Background/Introduction Isolated left ventricular dilation (ILVD), in the absence of concomitant LV systolic dysfunction, is a risk factor for new reduction in LV ejection fraction (LVEF) and adverse clinical outcomes. We assessed the performance of different machine learning (ML) models in predicting new reduction in LVEF in patients with ILVD. Methods A retrospective analysis of de-identified patient records in the Tempus Database with at least two transthoracic echocardiograms (TTE) conducted between January 2019 and December 2022 were retrospectively analyzed, with the follow up period ending in January 2023. Inclusion/exclusion criteria are shown in Figure 1A. Briefly, patients with ILVD were defined based on echocardiographic parameters as those with LVEF > 50% and LV end diastolic volume indexed (LVEDVi) > 62 mL/m2 for females and > 75 mL/m2 for males, without prior LVEF < 50% and/or grade 2/3 diastolic dysfunction. The endpoint was LVEF < 40% on future (≥90 days) TTE. Five hard voting ML models and one soft voting ensemble model were trained with data parsed from TTE reports (Figure 1B). Feature importance (SHAP) was calculated, and receiver operating characteristic (ROC) and Kaplan Meier curves evaluated the final model (Figure 1C-E). Results A total of 2,962 patients met the inclusion criteria. The median time from index to last TTE was 578 (IQR = 500) days. LVEF < 40% on follow up TTE was noted in 6% of patients (177 patients, median 478 days to endpoint, IQR = 535 days). Of the six models developed, the ensemble model had the highest ROC area under the curve (0.791, Figure 1D), precision (0.163), F1-score (0.266), and specificity (0.773). The ensemble model also had the lowest predicted prevalence (25.5%), although this was higher than the observed prevalence in the test set (5.7%). Patients predicted to develop LVEF < 40% had a 8.2x higher risk of developing LVEF < 40% within five years of index (p<0.001, Figure 1E). Feature importance showed that male gender, higher heart rate, lower LVEF at index TTE, higher LVEDVi, and lower tricuspid annular plane systolic excursion had the highest mean absolute impact on the model’s output probability (Figure 1C). Conclusion Using only data parsed from the index TTE, ensembling a model for the prediction of developing LVEF < 40% in patients with ILVD and without a history of LV diastolic or systolic dysfunction performed the best. Additional analyses including clinical variables such as comorbidities, symptoms, and medications may improve the model’s performance.

Comprehensive machine learning models for prediction of heart failure in 476,393 women and men from the UK Biobank reveal sex differences and underutilized risk factors

Abstract Background Machine learning (ML) models provide potential advantage over ‘traditional’ regression models in heart failure (HF) prediction. Objective To compare performances of Cox PH models and ML survival models for incident HF in men and women without prevalent ischemic heart disease (IHD). We also aimed to identify potential high-risk precursors otherwise ignored by conventional survival models, and to investigate differences between sex-specific models. Methods We included 476,393 participants (55.6% women) from the UK Biobank, after excluding participants with a history of HF or IHD, and defined sex-specific datasets. We predicted incident HF events using over 400 baseline characteristics. We constructed multivariable Cox PH models, which included all predictor variables and subsequently only those remaining after LASSO stability selection. We also developed two supervised ML models (Random Survival Forest (RSF), eXtreme Gradient Survival Boosting (XGBoost)). We identified the 15 most important sex-specific predictors in each model and performances were compared using the C-index. Models were validated using hold-out sets. Results During 12.3 ± 1.9 years of follow-up, 4680 (1.76%) women and 6631 (3.14%) men developed incident HF. XGBoost showed the best performance during model training (C-index, training: 0.89 in men, 0.97 in women; validation 0.77 in men, 0.80 in women). The multivariable Cox model performed second-best (C-index, training: 0.78 in men, 0.82 in women; validation: 0.76 in men, 0.78 in women). RSF performed slightly worse (C-index, training: 0.75 in men, 0.79 in women; validation: 0.75 in men, 0.79 in women) but did not show performance drop during validation. LASSO stability selection performed similar to RSF. Age, self-reported lifetime treatments and medications, cystatin-C, waist circumference and FEV1-scores were identified as strong risk factors in all models for both sexes. Reduced albumin levels and elevated HbA1c were more strongly associated with high risk in men, while elevated systolic BP showed higher importance in women. Traditional Cox models observed CRP as important only in men, while the ML models identified CRP as important for both sexes. Neutrophil count was considered a strong risk factor in both sexes in the traditional Cox models, yet it was not among the most important predictors in both ML models. Presence of other heart disease (which included a.o. pericardial disease, valve disorders and arrhythmias) was an important predictor variable only in the ML models. Conclusion ML models showed similar performance to Cox PH models for HF prediction. Despite this, differences in predictor importance were identified between models. Sex-specific risk predictors were found, and FEV1 score, which is not commonly included in existing models, was identified as an important risk factor. These results suggest that ML models may reveal additional insights that would otherwise remain unnoticed.

Machine learning for prediction of incident heart failure and heart failure hospitalisation: multi-cohort development, validation and association with clinical phenotypes

Abstract Background Delayed heart failure (HF) diagnosis is associated with adverse outcomes and costs of hospitalisation.(1,2) Whilst previous tools have been developed to predict incident HF,(3) risk of HF incidence may correlate poorly with risk of HF hospitalisation, which may be more important in targeting diagnostics. Purpose To develop and validate a novel decision support tool for prediction of incident HF and HF hospitalisation, and investigate association of score with HF with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). Methods We developed the models (FIND-HF) in UK primary care health record data from individuals aged ≥40 years without known HF (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and supervised machine learning models for prediction of 1- and 5- year HF and HF hospitalisation risk in the testing dataset with internal bootstrap validation. Models were externally validated for HF hospitalisation in data from a Taiwanese university hospital. Association of FIND-HF score with HF phenotypes of HFpEF and HFrEF was assessed in two prospective cohorts, the MATCH registry of patients diagnosed with HFrEF and the FIND-AF cohort of individuals at higher predicted risk of atrial fibrillation who underwent echocardiography. Results Of 565 284 UK individuals (mean age 68.9 (SD 12.2) years, 49.2% women), incidence of HF and HF hospitalisation was 1.1% and 0.5% at 1-year, and 6.6% and 3.4% at 5-years, respectively. Prediction performance was superior for machine learning algorithms compared with logistic regression, with best performance for XGBoost (area under receiver operating characteristic curve (AUROC) at 1 and 5-years: 0.755 and 0.723 for incident HF, and 0.738 and 0.711 for incident HF hospitalization). On external validation in 106,026 individuals in Taiwan (mean age 64.7 (SD 10.6) years, 52.8 % women), HF hospitalisation incidence was 0.71%, and 2.46% at 1- and 5- years, respectively, and discrimination performance was excellent (AUROC at 1- and 5-years: 0.834 and 0.843). When the FIND-HF score was applied to individuals in the MATCH cohort (n = 133, mean age 69.0 (SD 11.7) years, 38.3% women) and FIND-AF cohort (n = 82, mean age 71.6 (SD 7.5) years, 50.0% women), the distribution of score to clinical phenotypes demonstrated a binomial distribution (Figure 1), and likelihood of HFpEF diagnosis correlated with the numeric score (Figure 2). Conclusions The machine learning FIND-HF decision support tool can accurately identify individuals at risk of incident HF and HF hospitalisation, to enable new approaches for early detection and prevention. The variation of score amongst high risk individuals with different HF clinical phenotypes emphasizes the distinct pathophysiological pathways but shared adverse outcome profile across the HF syndrome.Figure 1Figure 2

The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation

Plasma protein binding (PPB) is closely related to pharmacokinetics, pharmacodynamics and drug toxicity. Existing models for predicting PPB often suffer from low prediction accuracy and poor interpretability, especially for high PPB compounds, and are most often not experimentally validated. Here, we carried out a strict data curation protocol, and applied consensus modeling to obtain a model with a coefficient of determination of 0.90 and 0.91 on the training set and the test set, respectively. This model (available on the OCHEM platform https://ochem.eu/article/29) was further retrospectively validated for a set of 63 poly-fluorinated molecules and prospectively validated for a set of 25 highly diverse compounds, and its performance for both these sets was superior to that of the other previously reported models. Furthermore, we identified the physicochemical and structural characteristics of high and low PPB molecules for further structural optimization. Finally, we provide practical and detailed recommendations for structural optimization to decrease PPB binding of lead compounds.

Large language models to facilitate pregnancy prediction after invitro fertilization.

We evaluated the efficacy of large language models (LLMs), specifically, generative pre-trained transformer-4 (GPT-4), in predicting pregnancy following invitro fertilization (IVF) treatment and compared its accuracy with results from an original published study. Our findings revealed that GPT-4 can autonomously develop and refine advanced machine learning models for pregnancy prediction with minimal human intervention. The prediction accuracy was 0.79, and the area under the receiver operating characteristic curve (AUROC) was 0.89, exceeding or being at least equivalent to the metrics reported in the original study, that is, 0.78 for accuracy and 0.87 for AUROC. The results suggest that LLMs can facilitate data processing, optimize machine learning models in predicting IVF success rates, and provide data interpretation methods. This capacity can help bridge the knowledge gap between data scientists and medical personnel to solve the most pressing clinical challenges. However, more experiments on diverse and larger datasets are needed to validate and promote broader applications of LLMs in assisted reproduction.

Predicting 1-year atrial fibrillation risk from a patch-based ambulatory ECG monitoring without atrial fibrillation

Abstract Introduction Early identification of atrial fibrillation (AF) can enable interventions that reduce cardiovascular events. However, AF remains undiagnosed in about 10-20% of affected individuals, and paroxysmal AF often remains undetected despite the use of in-clinic ECGs, consumer smartwatch ECGs, or ambulatory ECG monitoring. We therefore investigated whether ambulatory ECG patch-based monitoring without AF can predict AF in the subsequent 1-year post monitoring. Purpose This study aims to validate a machine-learning model for AF prediction within 1-year post-wear, based on the observation of non-AF single-lead ECG recordings. Methods We considered a clinically monitored cohort of 67,296 individuals wearing multiple single-lead ECG multi-day ambulatory monitoring patches as part of real-world care. For all selected individuals, the first patch recorded no occurrences of AF and was worn for a minimum of 6.5 days. We assess the risk of future AF based on the raw ECG, heartrate variability, frequency of ectopic beats, age and gender (ECG model), which is then compared with the risk assessment by a model based on age and gender (Baseline model). We then observe AF incidence on subsequent patches worn within one year from the first patch. We evaluated the model's accuracy by observing the number of AF cases identified (or missed) among the individuals with highest risk according to the model. Results In the clinically monitored cohort, a total of 3463 individuals experienced AF within the next year (incidence 5.1%) after a first AF-free monitoring period of a minimum 6.5 days. The receiver operating characteristic (ROC) curve shows an area under the curve (AUC) for the ECG model of 0.75 (95% CI: [0.74, 0.76]), and for the Baseline model of 0.66 (CI: [0.65, 0.67]). When selecting the top 20% (13,459) participants with the highest risk and verifying who developed AF in the following 1-year, we obtain positive prediction value (PPV) or precision of 12% and sensitivity (recall) of 46% for the ECG model (which correctly identifies 1,594 individuals with incident AF), and of 8% and 29%, respectively, for the Baseline model (p-value<0.001). (Figure) Conclusion A machine learning model using as input an AF-free single-lead ECG can quantify the risk of observing AF in a population including individuals with low burdens of paroxysmal or new onset of AF within 1 year. Further research and validation are needed to enhance the interpretability and clinical applicability of the model, while prediction may be further improved with clinical AF diagnoses ascertained from other data sources. The findings of this study have important implications for the early detection and management of AF, potentially enabling earlier intervention and improved outcomes in individuals with undiagnosed AF.

Machine learning prediction models for individualized technical success of chronic total occlusion revascularization using anatomical input

Abstract Introduction Chronic total occlusion revascularization (CTO PCI) remains a challenging procedure in the realm of percutaneous coronary interventions. Efforts such as implementing the hybrid algorithm for CTO crossing have been made. Interventional techniques are evolving, and complexity scores such as the J-CTO score might get outdated. Machine learning models can be used to overcome this limitation by constantly providing new data and improving the models by continuous data feeding. In this study, we introduced machine learning models to predict individualized technical success of CTO PCI using anatomical coronary information and baseline information. Methods Consecutive patients undergoing percutaneous coronary intervention of chronic total occlusions were enrolled in this study. Angiographic data and lesion characteristics from coronary angiography were extracted. Machine learning models were applied to predict technical success. A total of 271 patients were used to validate and train the models and 50 separate patients for testing. Data was normalized and fed to several classifying machine learning models including decision trees (DT), random forest (RF), extreme gradient boosting (XGB), CatBoost (CB) classifiers, and light gradient boosting (LGB). Finally, accuracy, precision, recall and F1-score of the selected hyperparameters and cross-validation was performed. Results CB was the best performing model with an accuracy of 0.81, precision of 0.83 and recall of 0.98. The F1 score was 0.90. RF was the second-best performing model with accuracy of 0.77, precision of 0.82, recall of 0.92 and F1 score 0.87. The tuned models performed modestly with RF having a low F1 score of 0.44, due to small sample size. In the tuned models. LGB had a higher F1 score and a relatively high accuracy (0.782) and precision (0.508). The feature importance matrix found age, CTO length, reference diameter to have the highest value among the lesion complexity parameters. Discussion: Overall, the normal-run CB was the best model for predicting the technical success after a CTO PCI with a high accuracy. The fine-tuned and cross-validated models performed modestly because of the relatively small, unbalanced dataset and overfitting. However, data showed promising first results in implementing machine learning into predicting interventional outcomes. Further studies like data augmentation and providing a weighted dataset would increase the model performance and provide a very precise statement whether a patient would have increased chances to have a successful PCI based on baseline anatomic information. Conclusion This is one of the first studies to predict technical success of CTO PCI using angiographic baseline parameters using machine learning. Future studies with larger patient cohorts are needed to involve deploying the selected model in larger patient cohorts including external validation and assessing its performance with new, unseen data.Figure 1.Machine learning modelsFigure 2.Feature importance matrix

Unlocking the future: machine learning models for predicting in-hospital mortality after transcatheter aortic valve replacement: a single-arm meta-analysis

Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

SLB - SMOTE logistic blending hybrid machine learning model for chronic polycystic ovary syndrome prediction with correlated feature selection

ABSTRACT Objective In this study, we aimed to develop a machine learning (ML) model for predicting Polycystic Ovary Syndrome (PCOS) based on demographic, clinical, and biochemical parameters. Methodology We collected data from Kaggle, which included information on age, body mass index, menstrual cycle length, follicle-stimulating hormone, hair growth, and more. Using this data, we trained several traditional ML and ensemble algorithms to predict PCOS. Results Among the traditional ML algorithms, Logistic Regression emerged as the best, boasting the highest accuracy of 0.91 and an AUC of 0.90. In ensemble algorithms, the Blending algorithm outperformed other ensemble methods, also achieving an accuracy of 0.91 and an AUC of 0.90, with a balanced precision and recall of 0.88. Significance of the Research These results establish Logistic Regression and the Blending algorithm as optimal choices for accurate and reliable PCOS prediction, demonstrating strong discriminative power and the ability to correctly classify PCOS cases.

Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis

Background/Objective: Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice. Methods: We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics. Results: The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling. Conclusions: While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.

Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications.

Author Correction: Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort

Author Correction: Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort

Multi-objective optimization of automotive seat frames using machine learning

The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.