Supervised Machine Learning Models Research Articles

On the Comparative Study of the Supervised Machine Learning Models

This research empirically compared the performance of three supervised machine learning models which are Multinomial Logistic Regression (MLR), Multilayer back propagated Neural Networks (MNN) and Multinomial Decision Trees (MDT) based on a tested data set for the opinion of Nigerian citizens during the period of naira redesign policy using classification matrix criterion. About 600 copies of questionnaires on the opinion of Nigerian citizens on their welfare at the period of naira redesign. The result showed that ANN outperformed other models with 94.4% correct classification rates, followed by the MLR with correct classification rates of 93.5% and lastly by MDT with correct classification rates of 90.0%.

Evaluation of Machine Learning Models for Predicting the Hot Deformation Flow Stress of Sintered Al-Zn-Mg Alloy

Abstract In predicting flow stress, machine learning (ML) offers significant advantages by leveraging data-driven approaches, enhancing material design, and accurately forecasting material performance. Thus, the present study employs various supervised ML models, including Linear Regression (Lasso and Ridge), Support Vector Regression (SVR), Ensemble Methods (RF, GB, XGB), and Neural Networks (ANN, MLP), to predict flow stress in the hot deformation of an Al-Zn-Mg alloy. The ML methodology involves sequential steps from data extraction to cross-validation and hyperparameter tuning, which is conducted using the hyperopt library. Model performance is assessed using average absolute relative error (AARE), root mean squared error (RMSE), and mean squared error (MSE). The results show that ensemble methods (RF, GB, XGB) and neural networks outperform traditional regression methods, demonstrating superior predictive accuracy. Visualization using half violin plots reveals the models' error ranges, with XGB consistently exhibiting the best performance. SVR, RF, GB, XGB, ANN, and MLP showed better performance than the Arrhenius model in the context of AARE and MSE metrics. Interestingly, SVR had a somewhat higher AARE of 1.89% and an MSE of 0.251 MPa2, while XGB had the lowest AARE of 0.2% and the lowest MSE of 0.011 MPa2. When ML models were evaluated using the skill score in relation to the Arrhenius model, XGB scored higher than SVM at 0.714, with a score of 0.986. In contrast Lasso and Ridge exhibited negative scores of −0.847 and −0.456, respectively.

A supervised machine learning model to select a cost-effective directional drilling tool

With the increased directional drilling activities in the oil and gas industry, combined with the digital revolution amongst all industry aspects, the need became high to optimize all planning and operational drilling activities. One important step in planning a directional well is to select a directional tool that can deliver the well in a cost-effective manner. Rotary steerable systems (RSS) and positive displacement mud motors (PDM) are the two widely used tools, each with distinct advantages: RSS excels in hole cleaning, sticking avoidance and hole quality in general, while PDM offers versatility and lower operating costs. This paper presents a series of machine learning (ML) models to automate the selection of the optimal directional tool based on offset well data. By processing lithology, directional, drilling performance, tripping and casing running data, the model predicts section time and cost for upcoming wells. Historical data from offset wells were split into training and testing sets and different ML algorithms were tested to choose the most accurate one. The XGBoost algorithm provided the most accurate predictions during testing, outperforming other algorithms. The beauty of the model is that it successfully accounted for variations in formation thicknesses and drilling environment and adjusts tool recommendations accordingly. Results show that no universal rule favors either RSS or PDM; rather, tool selection is highly dependent on well-specific factors. This data-driven approach reduces human bias, enhances decision-making, and could significantly lower field development costs, particularly in aggressive drilling campaigns.

Clinical and social characterization of patients hospitalized for COPD exacerbation using Machine Learning tools

Objective: This study aims to employ machine learning (ML) tools to cluster patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease (COPD) based on their diverse social and clinical characteristics. This clustering is intended to facilitate the subsequent analysis of differences in clinical outcomes.Methods: We analysed a cohort of patients with severe COPD from two Pulmonary Departments in north-western Spain using the k-prototypes algorithm, incorporating demographic, clinical, and social data. The resulting clusters were correlated with metrics such as readmissions, mortality, and place of death. Additionally, we developed an Intelligent Clinical Decision Support System (ICDSS) using a supervised ML model (Random Forest) to assign new patients to these clusters based on a reduced set of variables.Results: The cohort consisted of 524 patients, with an average age of 70.30 ± 9.35 years, 77.67% male, and an average FEV1 of 44.43 ± 15.4. Four distinct clusters (A-D) were identified with varying clinical-demographic and social profiles. Cluster D showed the highest levels of dependency, social isolation, and increased rates of readmissions and mortality. Cluster B was characterized by prevalent cardiovascular comorbidities. Cluster C included a younger demographic, with a higher proportion of women and significant psychosocial challenges. The ICDSS, using five key variables, achieved areas under the ROC curve of at least 0.91.Conclusions: ML tools effectively facilitate the social and clinical clustering of patients with severe COPD, closely related to resource utilization and prognostic profiles. The ICDSS enhances the ability to characterize new patients in clinical settings.

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature-based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black-box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time-series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.

Machine learning framework for selective and sensitive metal ion sensing with nitrogen-doped graphene quantum dots heterostructure

This study introduces a machine learning (ML) framework to optimize photodetector performance for sensor applications. Using the data from the fabricated photodetector with the heterostructure of nitrogen-doped graphene quantum dot and gold nanoparticles (Au@N-GQDs), various supervised ML models (more than 20 models) are trained and tested for the selection and refinement of the most effective algorithm for our work. Depending on the best-performed ML model, the optimized working wavelength of the photodetector is found for the detection of metal ions. Remarkably, the ML-based sensor shows a high level of selectivity and sensitivity in nM level towards Fe3+ ions in Brahmaputra river water. A strong alignment between model predictions and experimental outcomes validates the efficacy of the proposed ML-based framework. Moreover, data visualization techniques such as heatmaps, classification algorithms, and confusion matrices are introduced to identify the trends in the database. The mechanistic insight of the sensor performance towards Fe3+ ion sensing is further explained with heatmap analysis and experimental verification, which emphasizes the role of photo-induced charge transfer and Fe–O bond formation between metal ions and Au@N-GQDs due to the high electron affinity of Fe3+ ions.

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer

ObjectiveRenal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data on the concordance rate between RTB results and final pathology after surgery are unavailable. Therefore, we aimed to develop a machine learning algorithm to optimize RTB technique and to investigate the degree of concordance between RTB and surgical pathology reports. Materials and methodsWithin a prospectively maintained database, patients with indeterminate renal masses who underwent RTB at a single tertiary center were identified. We recorded and analyzed the approach (US vs. CT), the number of biopsy cores (NoC), and total core tissue length (LoC) to evaluate their impact on diagnostic outcomes. The K-Nearest Neighbors (KNN), a non-parametric supervised machine learning model, predicted the probability of obtaining pathological characterization and grading. In surgical patients, final pathology reports were compared with RTB results. ResultsOverall, 197 patients underwent RTB. Overall, 89.8% (n=177) and 44.7% (n=88) of biopsies were informative in terms of histology and grading, respectively. The discrepancy rate between the pathology results from renal tissue biopsy (RTB) and the final pathology report following surgery was 3.6% (n=7) for histology and 5.0% (n=10) for grading. According to the machine learning model, a minimum of 2 cores providing at least 0.8 cm of total tissue should be obtained to achieve the best accuracy in characterizing the cancer. Alternatively, in cases of RTB with more than two cores, no specific minimum tissue threshold is required. ConclusionsThe discordance rates between RTB pathology and final surgical pathology are notably minimal. We defined an optimal renal biopsy strategy based on at least 2 cores and at least 0.8 cm of tissue or at least 3 cores and no minimum tissue threshold. Patients summaryRTB is a useful test for kidney cancer, but it's not always perfect. Our study shows that it usually matches up well with what doctors find during surgery. Using machine learning can make RTB even better by helping doctors know how many samples to take. This helps doctors treat kidney cancer more accurately.

Moral decision making: Explainable insights into the role of working memory in autonomous driving

The intersection of Artificial Intelligence (AI) and moral philosophy presents unique challenges in the development of autonomous vehicles, particularly in scenarios requiring split-second ethical decisions. This study examines the relationship between working memory (WM) and moral judgments in simulated AV scenarios, quantifying the effects of varying cognitive load on utilitarian decision-making under different time constraints. We experimented with 336 participants, each completing 16 simulated driving trials presenting unique ethical dilemmas. Results reveal a complex interplay between cognitive load and ethical choices. Under high temporal pressure (1-second response window), utilitarian decisions decreased significantly from 92.77 % to 70.08 %. Extended time constraints led to increased utilitarian choices. Statistical analyses validated these findings across diverse ethical contexts. Chi-square tests revealed significant associations between WM load and utilitarian decisions in 1-second conditions, particularly for high-stakes scenarios. Logistic regression showed that WM significantly decreased the likelihood of utilitarian decisions in these scenarios. Six supervised machine learning models were employed, with Gaussian Naive Bayes achieving the highest predictive accuracy (82.2 % to 97.0 %) in distinguishing utilitarian decisions. Partial Dependence analysis revealed a strong negative correlation between WM and utilitarian decisions, especially in the 1-second interval. The 2-second interval emerged as potentially optimal for balancing time constraints and cognitive load. These findings contribute to the theoretical understanding of ethical decision-making under cognitive load and provide practical insights for developing ethically aligned autonomous systems, with implications for improving safety, optimizing takeover protocols, and enhancing the ethical reasoning capabilities of autonomous driving systems.

Real-Time Diagnostics on a QKD Link via QBER Time-Series Analysis

The integration of QKD systems in metro optical networks raises challenges that cannot be fully resolved with current technological means. In this work, we devised a methodology for identifying different types of impairments for a QKD link embedded in a communication network. Identification occurs in real time using a supervised machine learning model designed for this purpose. The model takes only QBER and SKR time-series data as the input, making its applicability not restricted to any specific QKD protocol or system. The output of the model specifies the working conditions for the QKD link, which is information that can be valuable for users and key management systems.

Integrating external stressors in supervised machine learning algorithm achieves high accuracy to predict multi-species biological integrity index of aquaculture wastewater

Monitoring and predicting the environmental impact of wastewater is essential for sustainable aquaculture. The environmental DNA metabarcoding-integrated supervised machine learning (SML) algorithm is an alternative method for ecological quality assessment and prediction. However, the ecological integrity of aquaculture wastewater and available effective input features for prediction remain unclear. Here, we used the multispecies biological integrity index (Ms-IBI) to provide a detailed categorization, identifying over half of the samples (53.85 %) as highly impacted, emphasizing the urgency to address ecological degradation; Ms-IBI emerged as a reliable label for SML models. By condensing 410 effective indicators and integrating 25 core operational taxonomic unit features with external stressors, an accuracy of 0.78 and R2 of 0.96 was achieved. Utilizing only external stressors yielded a comparably good performance with fewer input features, obtaining an accuracy of 0.74 and an R2 of 0.91. The integration of external stressors in this study highlights a practical predictive method that meets the ecological quality requirements of aquaculture wastewater, aiding the reversal of global ecological decline.

Long-read RNA sequencing reveals allele-specific N6-methyladenosine modifications.

Long-read sequencing technology enables highly accurate detection of allele-specific RNA expression, providing insights into the effects of genetic variation on splicing and RNA abundance. Furthermore, the ability to directly sequence RNA using the Oxford Nanopore technology promises the detection of RNA modifications in tandem with ascertaining the allelic origin of each molecule. Here, we leverage these advantages to determine allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNA. We utilized human and mouse cells with known genetic variants to assign allelic origin of each mRNA molecule combined with a supervised machine learning model to detect read-level m6A modification ratios. Our analyses revealed the importance of sequences adjacent to the DRACH-motif in determining m6A deposition, in addition to allelic differences that directly alter the motif. Moreover, we discovered allele-specific m6A modification (ASM) events with no genetic variants in close proximity to the differentially modified nucleotide, demonstrating the unique advantage of using long reads and surpassing the capabilities of antibody-based short-read approaches. This technological advancement promises to advance our understanding of the role of genetics in determining mRNA modifications.

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.

Prediction of atrial fibrillation recurrence after ablation: the impact of machine learning and recurrence during the blanking period

Abstract Introduction Pulmonary vein isolation is superior to antiarrhythmic drugs for rhythm control in patients with atrial fibrillation (AF). Nevertheless, recurrences within the first year are still significant. Several clinical risk scores were developed to predict AF recurrence; however, despite good results in development cohorts, the results in external validation are usually poor, limiting the general applicability of these scores. Purpose We aimed to develop machine learning (ML) models to predict AF recurrences within the first year after catheter ablation and compare their performance with conventional risk scores. Methods We used a retrospective dataset of a tertiary hospital center, including all consecutive patients submitted to the first AF ablation between 2017 and 2021. The dataset included 76 features: patient characteristics, medical history, baseline echocardiography, calcium score, procedure variables, and early recurrence (ER) during the 90-day blanking period. The outcome was defined as the occurrence of an electrocardiographically documented late recurrence (LR) of atrial tachycardia/AF > 30s between 3-12 months. Three different supervised ML models (penalized logistic regression, random forests, and XGBoost) were developed on the training cohort, and hyperparameters were tuned using 10-fold cross-validation. A testing dataset was held out to estimate the final performance (25%). The following risk scores were calculated: APPLE, CAAP-AF, and BASE-AF2 (the last includes ER). Areas under the curve (AUC) of receiver operating characteristic curves were compared using DeLong’s test. Results 679 patients were included: 62.7% males, median age of 59±16 years, and 78.2% with paroxysmal AF. The median time from diagnosis to ablation was 2±4 years. 74.2% underwent radiofrequency ablation and the remaining cryoablation. ER occurred in 12.5%, and most of these patients also experienced LR (68.7 vs 19.6% in those without ER, p<0.001). LR was observed in 25.6%. The XGBoost model showed the best performance with an AUC 0.774, 95% confidence interval (CI) 0.688–0.844) outperforming existing scores (picture 1): APPLE score AUC 0.607, 95% CI 0.562–0.653, p-value<0.001; CAAP-AF score AUC 0.622, 95% CI 0.576–0.668, p-value=0.002; BASE-AF2 AUC 0.628, 95% CI 0.581–0.675, p-value= 0.003. Variable importance analysis showed a significant drop in the performance of the models when ER was not considered, indicating its high importance in predicting LR (picture 2). Conclusions Recurrence during the blanking period was the most important predictor of LR in our population. The ML model was superior to conventional risk scores. ML models might be an essential tool to improve the prediction of outcomes and clinical decision-making for optimal follow-up.ROC curvesFeature Importance

A robust deep learning approach for identification of RNA 5-methyluridine sites

RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning-based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets. We used three descriptor groups: nucleic acid composition, pseudo nucleic acid composition, and physicochemical properties, which include five feature extraction methods ENAC, Kmer, DPCP, DPCP type 2, and PseDNC. Initially, we aggregated all the feature extraction methods and created a new merged set. Three hybrid models were developed employing deep-learning methods and evaluated through 10-fold cross-validation with seven evaluation metrics. After a comprehensive evaluation, the GRUpred-m5U model outperformed the other applied models, obtaining 98.41% and 96.70% accuracy on the two datasets, respectively. To our knowledge, the proposed model outperformed all the existing state-of-the-art technology. The proposed supervised machine learning model was evaluated using unsupervised machine learning techniques such as principal component analysis (PCA), and it was observed that the proposed method provided a valid performance for identifying m5U. Considering its multi-layered construction, the GRUpred-m5U model has tremendous potential for future applications in the biological industry. The model, which consisted of neurons processing complicated input, excelled at pattern recognition and produced reliable results. Despite its greater size, the model obtained accurate results, essential in detecting m5U.

Machine learning model using cardiovascular magnetic resonance to predict cardiovascular events in asymptomatic patients with known CAD

Abstract Background Despite declining rates, recurrent cardiovascular events continue to pose significant mortality and morbidity risks in patients with obstructive coronary artery disease (CAD). Traditional prognostic assessments in these patients rely on limited clinical and imaging data. Although several studies have shown the strong prognostic value of stress cardiovascular magnetic resonance (CMR) in asymptomatic patients with known CAD, no dedicated score using CMR was established. Machine learning (ML) methods present an opportunity to leverage a wider array of variables to address this gap. Purpose This study sought to investigate the feasibility and accuracy of ML using stress CMR and clinical data to predict major adverse cardiovascular events (MACE) over a 10-year period in patients with known obstructive CAD, comparing its performance with traditional statistical methods. Methods Between 2009 and 2011, consecutive asymptomatic patients with known obstructive CAD referred for vasodilator stress CMR in two independent centres were followed for the occurrence of MACE defined by cardiovascular mortality or recurrent non-fatal myocardial infarction (MI). Known obstructive CAD was defined by a history of percutaneous coronary intervention, coronary artery bypass surgery, or MI. The first center (N=866) was designated as the derivation cohort for variable selection and model building. The second center (N=224) was used as the validation cohort to evaluate the performance of the final ML-model. Various feature selection methods (Random Forest [RF], XGBoost, and Boruta) were employed to identify robust and informative variables. A final RF model was trained on the derivation cohort and evaluated on the validation cohort. Performance was compared with a standard generalized logistic regression model (GLM), utilizing the receiver operating characteristics (ROC) and the precision-recall (PR) curves as well as the area under the curves (AUC). Results In the derivation cohort (age 67±7 years, LVEF 43±8%, 81% male), 243 (28%) out of 866 patients encountered MACE during the 10-year follow-up period while in the validation cohort (age 67±7 years, LVEF 43±7%, 84% male), 53 (24%) out of 224 patients experienced MACE. Through feature selection method in the derivation cohort, one clinical and four CMR variables emerged as key predictors: number of ischemic segments, number of late gadolinium enhancement (LGE) segments, left ventricular ejection fraction, left ventricular end-diastolic diameter indexed, and age (Figure 1). Using those 5 variables, the RF model significantly outperformed the GLM, demonstrating superior predictive accuracy (AUROC: 0.89 vs. 0.73; PRAUC: 0.78 vs. 0.58, all p<0.001) in the validation cohort (Figure 2). Conclusions A supervised machine learning model using RF, integrating age and stress-CMR data, outperformed traditional statistical methods in predicting 10-year MACE for asymptomatic patients with known CAD.Figure1:Variable selectionFigure2:Models performance on validatio

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

Predicting Housing Prices Using Supervised Machine Learning Models

Abstract: Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.

A Supervised Ml Algorithm for Detecting and Predicting Fraud Credit Card Transactions

A Credit card fraud presents an escalating threat in today's digital economy, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional detection methods are increasingly inadequate due to the rise in online transactions and the evolving complexity of fraudulent activities. This research aims to create a reliable supervised machine learning model designed to detect and predict fraudulent credit card transactions in real-time. Our approach leverages advanced algorithms and extensive datasets to enhance transaction security, thereby mitigating financial risks. Current systems, like FICO's Fraud Detection System (FDS), utilize a range of supervised learning techniques, including Decision Trees, Random Forests, and Neural Networks. However, they face limitations, including data quality issues, high false positive rates, and the need for continuous retraining to adapt to evolving fraud patterns. To address these challenges, we propose an innovative anomaly detection method based on the Isolation Forest algorithm. Unlike traditional methods that rely on distance and density measures, Isolation Forests isolate anomalies by randomly selecting features and split values, making it more efficient in identifying fraudulent transactions, especially in imbalanced datasets. The proposed system boasts low linear time complexity and minimal memory requirements, enabling the construction of high-performing models even with large datasets. The research demonstrates that the Isolation Forest approach significantly outperforms traditional models in detecting credit card fraud, offering a promising solution for enhancing security in the financial ecosystem.

Analyzing Unimproved Drinking Water Sources and Their Determinants Using Supervised Machine Learning: Evidence from the Somaliland Demographic Health Survey 2020

Access to clean and safe drinking water is a fundamental human right. Despite global efforts, including the UN’s “Water for Life” program, a significant portion of the population in developing countries, including Somaliland, continues to rely on unimproved water sources. These unimproved sources contribute to poor health outcomes, particularly for children. This study aimed to investigate the factors associated with the use of unimproved drinking water sources in Somaliland by employing supervised machine learning models to predict patterns and determinants based on data from the 2020 Somaliland Demographic and Health Survey (SHDS). Secondary data from SHDS 2020 were used, encompassing 8384 households across Somaliland. A multilevel logistic regression model was applied to analyze the individual- and community-level factors influencing the use of unimproved water sources. In addition, machine learning models, including logistic regression, decision tree, random forest, support vector machine (SVM), and K-nearest neighbor (KNN), were compared in terms of accuracy, sensitivity, specificity, and other metrics using cross-validation techniques. This study uses supervised machine learning models to analyze unimproved drinking water sources in Somaliland, providing data-driven insights into the complex determinants of water access. This enhances predictive accuracy and informs targeted interventions, offering a robust framework for addressing water-related public health issues in Somaliland. The analysis identified key determinants of unimproved water source usage, including socioeconomic status, education, region, and household characteristics. The random forest model performed the best with an accuracy of 93.57% and an area under the curve (AUC) score of 98%. Decision tree and KNN also exhibited strong performance, while SVM had the lowest predictive accuracy. This study highlights the role of socioeconomic and community factors in determining access to clean drinking water in Somali Land. Factors such as age, education, gender, household wealth, media access, urban or rural residence, poverty level, and literacy level significantly influenced access. Local policies and resource availability also contribute to variations in access. These findings suggest that targeted interventions aimed at improving education, infrastructure, and community water management practices can significantly reduce reliance on unimproved water sources and improve the overall public health.

Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms.

Liver cancer, particularly Hepatocellular carcinoma (HCC), remains a significant global health concern due to its high prevalence and heterogeneous nature. Despite the existence of approved drugs for HCC treatment, the scarcity of predictive biomarkers limits their effective utilization. Integrating diverse data types to revolutionize drug response prediction, ultimately enabling personalized HCC management. In this study, we developed multiple supervised machine learning models to predict treatment response. These models utilized classifiers such as logistic regression (LR), k-nearest neighbors (kNN), neural networks (NN), support vector machines (SVM), and random forests (RF) using a comprehensive set of molecular, biochemical, and immunohistochemical features as targets of three drugs: Pantoprazole, Cyanidin 3-glycoside (Cyan), and Hesperidin. A set of performance metrics for the complete and reduced models were reported including accuracy, precision, recall (sensitivity), specificity, and the Matthews Correlation Coefficient (MCC). Notably, (NN) achieved the best prediction accuracy where the combined model using molecular and biochemical features exhibited exceptional predictive power, achieving solid accuracy of 0.9693 ∓ 0.0105 and average area under the ROC curve (AUC) of 0.94 ∓ 0.06 coming from three cross-validation iterations. Also, found seven molecular features, seven biochemical features, and one immunohistochemistry feature as promising biomarkers of treatment response. This comprehensive method has the potential to significantly advance personalized HCC therapy by allowing for more precise drug response estimation and assisting in the identification of effective treatment strategies.