Learning Predictive Models Research Articles

An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern

Intermittent fasting is currently a highly sought-after dietary pattern. To explore the potential biomarkers of intermittent fasting, untargeted metabolomics analysis of fecal metabolites in two groups of mice, intermittent fasting and normal feeding, was conducted using UPLC-HRMS. The data was further analyzed through interpretable machine learning (ML) to data mine the biomarkers for two dietary patterns. We developed five machine learning models and results showed that under three-fold cross-validation, Random Forest model was the most suitable for distinguishing the two dietary patterns. Finally, Shapely Additive exPlanations (SHAP) were explored to perform a weighted explanatory analysis on the Random Forest model, and the contribution of each metabolite to the model was calculated. Results indicated that Ganoderenic Acid C is the potential biomarkers to distinguish the two dietary patterns. Our work provides new insights for metabolic biomarker analysis and lays a theoretical foundation for the selection of a healthieir dietary lifestyle.

Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model

In view of the problem of the insufficient performance of deep learning models in time series prediction and poor comprehensive space–time feature extraction, this paper proposes a diagnostic method (CNN-LSTM-GRU) that integrates convolutional neural network (CNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) models. In this study, a convolutional neural network (CNN) model is used to process two-dimensional image data in both time and frequency domains, and a convolutional core attention mechanism is introduced to extract spatial features, such as peaks, cliffs, and waveforms, from the samples. A long short-term memory (LSTM) network is embedded in the output processing of the convolutional neural network (CNN) to analyze the long-sequence variation characteristics of rolling bearing vibration signals and enable long-term time series prediction by capturing long-term dependencies in the sequence. In addition, a gated recurrent unit (GRU) is used to refine long-term time series predictions, providing local fine-tuning and improving the accuracy of fault diagnosis. Using a dataset obtained from Case Western Reserve University (CWRU), the average accuracy of CNN-LSTM-GRU fault vibration is greater than 99%, and its superior performance in a noisy environment is demonstrated.

Interpretable machine learning models for concrete compressive strength prediction

Interpretable machine learning models for concrete compressive strength prediction

Voltage Mining for (De)lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage.

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulas is trained and shows state-of-the-art performance when compared to two established composition-based ML models for material properties predictions, Roost and CrabNet.

Comparative Study of Different Machine Learning Models for Heat Transfer Performance Prediction of Evaporators in Modular Refrigerated Display Cabinets

This study explores the potential of machine learning models to predict evaporator heat transfer performance in Modular Refrigerated Display Cases (MRDCs). Six experimental datasets from MRDC systems were analyzed to compare the efficacy of six machine learning models: Linear Regression, Decision Tree Regression, Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM). The findings indicate that the ensemble tree-based models, LightGBM and RF, are particularly effective in predicting evaporator heat transfer performance. These models demonstrate high accuracy and robustness, effectively capturing the nonlinear relationship between the evaporator temperature and heat transfer coefficient. Moreover, LightGBM and RF exhibit notable stability and adaptability in scenarios of limited data availability and elevated noise levels. Their consistent predictive accuracy across different experimental conditions highlights their suitability for complex refrigeration systems. This research provides essential insights for optimizing MRDC evaporator performance, establishing a theoretical and data-driven foundation for energy-efficient enhancements and intelligent management within cold chain systems.

PerSceptoMed 1.0: An advanced machine learning model for demographic-based antibiotic susceptibility prediction in Pseudomonas aeruginosa infections

BackgroundAntimicrobial resistance caused by Pseudomonas aeruginosa is a pressing global health issue impacting both hospitals and communities. MethodologyWe analyzed demographic and susceptibility data from 1087 samples, and tested them against five different antibiotics at the Institute of Medical Sciences & SUM Hospital. Eight machine learning algorithms (linear: multiple linear regression, generalized linear model-elastic net regularization, support vector machine-linear kernel, linear discriminant analysis; non-linear: support vector machine-radial basis function kernel, decision tree, k-nearest neighbors, kernel k-nearest neighbors), were evaluated to develop the PerSceptoMed 1.0 model for susceptibility prediction. Performance metrics such as accuracy, Matthews Correlation Coefficient, Hamming loss, 0/1 loss, and Receiver Operating Characteristic curves were computed. Statistical analysis was conducted to compare model performance. ResultsGender disparities were observed, with male patients showing higher infection rates, potentially due to occupational exposure, microbiome differences, comorbidities, and healthcare-seeking behaviors. age, clinical status, and antibiotic susceptibility were significant factors, complicating treatment strategies. Infections extended to outpatient departments, indicating a shift from hospital to community settings. Kernel k-nearest neighbors exhibited strong predictive capabilities, improving accuracy from 84.39% pre-balancing to 91.01% post-balancing, and an Area Under the Curve of 0.92. Statistical tests showed significant performance variations among models. ConclusionThe study highlights the importance of gender-sensitive approaches in infection management and the superior performance of nonlinear models in decoding complex data patterns. PerSceptoMed 1.0 demonstrates promise in enhancing antimicrobial resistance management by providing actionable insights based on demographic data.

The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior

This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists’ purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emotionally analyze tourism review information, dividing it into positive, negative, or neutral to understand tourists’ attitudes towards purchasing products and services. Secondly, a High-Performance Network (HPN) model is constructed based on factors that affect tourists’ purchases. The relationship among tourists, products, and word of mouth (WOM) is represented as a complex network to analyze and predict event occurrence patterns and influencing factors in tourism electronic word-of-mouth (EWOM) data. The construction of the model considers various factors, such as the spread of WOM, the impact of price, etc., to reveal the complex relationships among tourists, WOM, products, etc. Finally, the Recurrent Neural Network (RNN) model is combined with the Backpropagation (BP) model, the time series data is processed with the help of the gated recurrent unit, and the HPN model is trained and evaluated. The Yelp dataset is employed to verify the accuracy and feasibility of the model, which contains the score and review data of many tourist destinations. The results reveal that price, WOM, and destination are one of the main factors influencing tourists’ purchasing behavior, with WOM being the most significant. Positive WOM reviews remarkably increase product sales, while negative WOM has the opposite effect. The minimum expectation for age, occupation, education, personal monthly income, and tourists’ willingness to purchase is 0.00, and the minimum expectation for gender factors is 0.31. The RNN-BP hybrid model has higher accuracy and predictive ability, which is 1.73% and 2.30% more accurate than single models and traditional machine learning predictive models. In short, this study contributes to a better understanding travelers’ needs and preferences to optimize products and services and improve market competitiveness. In addition, the methods and models of this study can also be applied in EWOM data mining in other fields.

Machine Learning-Based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) visit. A retrospective study was conducted on patients, aged younger than 18 years, who visited a tertiary university-affiliated hospital ED due to FN between January 2004 and August 2022. ML models, based on XGBoost, were developed for BSI and SS prediction. After applying the exclusion criteria, we identified 4423 FN events during the study period. We identified 195 (4.4%) BSI and 107 (2.4%) SS events. The BSI and SS models demonstrated promising performance, with area under the receiver operating characteristic curve values of 0.87 and 0.88, respectively, which were superior to those of the logistic regression models. Clinical features, including body temperature, some laboratory results, vital signs, and diagnosis of acute myeloblastic leukemia were identified as significant predictors. The ML-based prediction models, which use data obtainable at ED visits may be valuable tools for ED physicians to predict BSI or SS.

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder

Exploring deep learning models for roadside landslide prediction: Insights and implications from comparative analysis

This study undertakes a comparative analysis of four distinct deep learning models, i.e., Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), in the context of roadside landslide prediction, aiming to provide comprehensive insights into their strengths and weaknesses. A geospatial dataset from the Lai Châu province, Vietnam, with thirteen environmental factors (elevation, aspect, slope, curvature, topographic wetness index, stream power index, flow accumulation, geology, normalized difference vegetation index, maximum rainfall, average annual rainfall, and proximity to faults and rivers) and 284 road-along landslides was considered for analysis. Our modeling efforts yielded invaluable insights into the performance of these models during both training and validation phases. The DNN model emerged as the frontrunner in the training phase, boasting the highest area under the curve (AUC) of 0.94, accuracy of 87.47%, kappa of 0.748, and lowest RMSE of 0.125. However, during validation, the CNN model outshone others, exhibiting the highest AUC of 0.88 and overall accuracy of 80.00%. Despite variations in performance metrics across phases, CNN consistently demonstrated robust predictive prowess. The findings of this study underscore the significance of selecting appropriate machine learning models tailored to specific contexts and objectives. Moreover, they contribute valuable insights for decision-makers and researchers alike, ultimately aiming to enhance the safety and resilience of communities inhabiting landslide-prone areas. Moving forward, future research directions may explore ensemble methods, novel architectures, and interpretability techniques to further advance predictive accuracy and applicability in roadside landslide susceptibility modeling.

Automatic kidney disease prediction using deep learning techniques

The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection.

Deep Learning Model for Prediction of Dementia Severity based on EEG Signals

This study aimed to determine variations in the electroencephalograms (EEGs) of 15 individuals who were diagnosed with mild cognitive impairment (MCI) following stroke, 5 individuals suffering from vascular dementia (VD) and 15 healthy normal control (NC) individuals who performed a working memory task. Conventional filters including notch and bandpass filters were utilised to remove noise from the EEG data. The proposed method comprises computing the estimates of the attention entropy (AttEn), bubble entropy (BubbEn) and symbolic dynamic entropy (SyDyEn) of univariate data sequence features. The long short-term memory (LSTM) deep learning neural network was used to automatically classify dementia severity through noninvasive EEG-based recordings. The LSTM classification result with AttEn exceeds an average of 88.9% than BubbEn and SyDyEn, with classification results of 69.2% and 77.7%, respectively. The analysis of the brain EEG-based dementia severity dataset suggests that AttEn could potentially serve as a biomarker for detecting dementia severity. AttEn can capture relevant patterns and features in the EEG data and may be indicative of the severity of dementia with LSTM RNN to differentiate patients with VD, patients with MCI and NC individuals.

A machine learning predictive model for bushfire ignition and severity: The Study of Australian black summer bushfires

A machine learning predictive model for bushfire ignition and severity: The Study of Australian black summer bushfires

Ensemble Learning Prediction Model for Lithium-ion Battery Remaining Useful Life Based on Embedded Feature Selection

Ensemble Learning Prediction Model for Lithium-ion Battery Remaining Useful Life Based on Embedded Feature Selection

Machine learning predictive model to estimate the photo-degradation performance of stannates and hydroxystannates photocatalysts on a variety of waterborne contaminants

In this work, a comprehensive machine learning (ML) methodology was used to predict the degradation efficiency of different stannate and hydroxystannate photocatalysts on a wide range of waterborne pollutants. The structural, atomic features along with molecular fingerprints (MF) were used as descriptors of the crystalline phase of the photocatalysts and the organic compounds, respectively. The encoded features of the photocatalysts and contaminants along with the experimental variables of the degradation process are input to two ML models, named as RF (random forest) and KNN (K nearest neighbor). The RF model has achieved a very good prediction of the photocatalytic degradation efficiency (%) by different photocatalysts over a wide range of organic contaminants. The RF model performance was investigated by applying two different training strategies. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Two illustrative applications on the use of the ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications were provided.

A machine learning-based method for predicting the shear behaviors of rock joints

In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress values and shear stress-displacement curves of rock joints. The database used contained 693 records of peak shear stress and 162 original shear stress-displacement curves derived from direct shear tests. The results demonstrated that the MLPMs provided reliable predictions for shear stress, with the mean squared errors (MSEs) between their predicted and measured shear stress varying from 0.003 to 0.069 and the coefficients of determination (R2 values) varying from 0.964 to 0.998. The feature importance values indicate that the joint surface roughness coefficient (JRC) is the most important influential factor in determining the peak shear stress, followed by the joint wall compressive strength (JCS), basic friction angle (φb), and shear surface area (As). Similarly, for the shear stress-displacement curve, the JRC is the dominant factor, followed by As, φb, and JCS. Additional direct shear tests were conducted for model validation. The validation shows that the MLPM predictions demonstrate improved consistency with the experimental results in relation to both the peak shear stress and peak shear displacement.

Machine Learning Prediction of Quality of Life: Insight from Property Crime and Tropical Climate Analysis

<p align="center">This study delves into predicting the quality of life, a widely researched concept spanning various domains, employing mixed methodologies and indicators. Common predictors of overall quality of life encompass health status, socioeconomic factors, subjective well-being, and environmental conditions. Consequently, this research investigates the potential of utilizing machine learning prediction models for quality-of-life prediction, focusing on two key indicators.Five machine learning algorithms—Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Machine—are empirically compared. Their performance in predicting quality of life is assessed based on property crime and tropical climate (temperature) attributes. Despite an initially low correlation value, temperature yields valuable insights for specific algorithms, enhancing predictive accuracy. This underscores the intricate and nuanced impact of seemingly unrelated factors in machine learning. The Support Vector Machine emerges as the top-performing algorithm, followed by Random Forest and Decision Tree. This paper offers a foundational research framework to aid educators and researchers in exploring quality of life prediction in-depth, utilizing property crime and temperature as key factors.</p>

Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model

Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model

Concordance analysis between deep learning model predictions and electrocardiography thresholds from cardiology guidelines

Abstract Deep learning models for the classification of electrocardiograms (ECGs) are able to learn disease-specific patterns, but they are rarely implemented in medical practice due to their "black box" nature. Post-hoc explainable artificial intelligence (XAI) methods compute regions of interest (ROI) which are of importance for a model’s decision making. However, it needs to be further analyzed whether a model focuses on the morphological or rhythmical information within the ROIs. We evaluate a pre-trained ResNet for sinus bradycardia (SB) and sinus tachycardia (ST) classification on the PTBXL dataset using the XAI method Integrated Gradients. We compare the confidence of the model predictions to ECG features used by clinicians using correlation analysis. Correlation is highest for RR intervals (SB: 0.44) and atrial as well as ventricular heart rates (ST: 0.51), with the majority exceeding clinical thresholds for both disorders, indicating that the model learned rhythmical features. Except for QT intervals in ST classification, morphological features such as duration and amplitudes of P-/T-waves do not show any correlation.

A Machine Learning Model for Prostate Cancer Prediction in Korean Men

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability.Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio.Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863.Conclusions: Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.