Classification models combined with optimized features for mental stress prediction

Early diagnosis and treatment is very essential in monitoring Brain tumor using MRI images. Convolutional Neural Networks (CNN) and Machine Learning (ML) classifiers have been widely used but there is not much work on how feature selection techniques would affect the performance of the CNN. Secondly, there is a need for investigation concerning small dataset adaptability and ML-CNN comparisons. To improve the classification accuracy, we integrate Univariate, Recursive Feature Elimination (RFE), Recursive Feature Elimination with Cross Validation (RFECV) with CNN in this study. Preprocessing, feature extraction & selection was carried out on the dataset consisting of 253 MRI images and they are classified using CNN and ML models (Logistic Regression, Decision Tree, Random Forest, Naïve Bayes). With the results 96%, CNN with Univariate Feature Selection performed better than ML classifiers, and other selection techniques. The results demonstrate that feature selection is necessary to get the best performance out of CNN models operating on small datasets. Future studies should be based on different deep learning architectures to improve classification and application in other datasets.

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.

BackgroundEarly recurrence in colorectal cancer liver metastases (CRLM) typically correlates with significantly worse survival outcomes. There is a strong demand for developing robust and interpretable approaches to assist clinicians in identifying patients at high risk of recurrence.MethodsIn this study, we utilized clinical CT images and associated clinical data from 197 CRLM patients, provided as DICOM images. A total of 993 radiomic features, including shape, texture, and first-order characteristics, were extracted. Eight machine learning models were trained and validated: Random Forest (RF), Multilayer Perceptron (MLP), K-nearest Neighbors (KNN), Extremely Randomized Trees (ET), AdaBoost, Decision Trees (DT), Gradient Boosting (GB), and Linear Discriminant Analysis (LDA).ResultsIn predicting tumor recurrence within one year, the ET model showed the best performance using only radiomic features, with an AUC of 0.9667. RF and GB also performed well, achieving AUCs of 0.9558 and 0.9227, respectively. When combining radiomic and clinical features, the performance of all models improved in terms of AUC. Specifically, the Random Forest (RF) model achieved the highest AUC of 0.9672, followed by Gradient Boosting (GB) with an AUC of 0.9646, and Extra Trees (ET) with an AUC of 0.9459.ConclusionWe developed a CT-based machine learning model, using the Random Forest algorithm, that combines clinical (e.g., age, carcinoembryonic antigen, bilobar disease) and radiomic features (e.g., selected features included texture-based metrics such as the 90th percentile of first-order statistics in the high–low–low (HLL) wavelet-decomposed image, and Run Entropy from the gray-level run length matrix (GLRLM) in the low–low–low (LLL) sub-band.) to predict early recurrence after hepatectomy in patients with colorectal liver metastasis (CRLM). This model has the potential to guide personalized postoperative surveillance. However, limitations such as the retrospective single-center design and relatively small sample size may affect the generalizability of the findings. Further validation in larger, multi-center cohorts is warranted to confirm its clinical utility.

Discovery of new antibiotics is the need of the hour to treat infectious diseases. An ever-increasing repertoire of multidrug-resistant pathogens poses an imminent threat to human lives across the globe. However, the low success rate of the existing approaches and technologies for antibiotic discovery remains a major bottleneck. In silico methods like machine learning (ML) deem more promising to meet the above challenges compared with the conventional experimental approaches. The goal of this study was to create ML models that may be used to successfully predict new antimicrobial compounds. In this article, we employed eight different ML algorithms namely, extreme gradient boosting, random forest, gradient boosting classifier, deep neural network, support vector machine, multilayer perceptron, decision tree, and logistic regression. These models were trained using a dataset comprising 312 antibiotic drugs and a negative set of 936 non-antibiotic drugs in a five-fold cross validation approach. The top four ML classifiers (extreme gradient boosting, random forest, gradient boosting classifier and deep neural network) were able to achieve an accuracy of 80 per cent and above during the evaluation of testing and blind datasets. We aggregated the top performing four models through a soft-voting technique to develop an ensemble-based ML method and incorporated it into a freely accessible online prediction server named ABDpred ( http://clinicalmedicinessd.com.in/abdpred/ ).

BackgroundDepression is a prevalent and challenging mental health condition, often resistant to traditional therapies. Repetitive transcranial magnetic stimulation (rTMS) is an FDA-approved treatment for resistant depression, but predicting treatment response remains difficult. Metabolic factors, such as obesity, diabetes, and associated inflammatory pathways, are increasingly recognized as influencing depression treatment outcomes. Leveraging machine learning may provide insights into how these factors predict rTMS efficacy, enabling more personalized treatments.Aims & ObjectivesThis study aims to identify key metabolic and clinical factors that predict rTMS treatment outcomes in patients with major depressive disorder (MDD) and bipolar disorder (BD). To achieve this, machine learning models were employed to develop predictive frameworks tailored for small participant groups.MethodSixty-eight patients (28 with BD) participated, undergoing 12 sessions of rTMS targeting the left dorsolateral prefrontal cortex. Participants were assessed at baseline, Week 4, and Week 12 using the Hamilton Depression Rating Scale (HDRS). Predictive models were developed using recursive feature elimination (RFE) and four machine learning algorithms: Random Forest, Gradient Boosting, Extra Trees, and XGBoost. Key features identified included body mass index (BMI), cholesterol ratios, and insulin resistance markers. SHAP (Shapley Additive Explanation) values were applied for model interpretation.ResultsAt Week 4, the remission rate was 59.6%, with BMI, systolic blood pressure (SBP), and LDL/HDL ratio emerging as significant predictors. The Extra Trees model achieved the highest performance (AUC = 0.766, accuracy = 0.726). At Week 12, remission was 61.5%, and predictors included insulin resistance (HOMA-IR), brain-derived neurotrophic factor (BDNF), and LDL/HDL ratio. The Gradient Boosting model excelled (AUC = 0.862, accuracy = 0.773). SHAP analysis highlighted BMI and HOMA-IR as the most influential factors at Weeks 4 and 12, respectively.Discussion & ConclusionsThe study underscores the impact of metabolic health on depression treatment outcomes. Metabolic dysregulation, particularly obesity and insulin resistance, may impair neuroplasticity and inflammation pathways, complicating treatment. BDNF's role in neurogenesis and metabolism was also highlighted as pivotal for recovery. Machine learning models demonstrated robust predictive performance, emphasizing the potential of integrating clinical and metabolic data to refine rTMS treatment approaches.Metabolic and clinical factors significantly influence rTMS outcomes in depression. Machine learning models, especially Extra Trees and Gradient Boosting, offer promising tools for individualized treatment planning. Future research should validate these findings in larger populations and explore additional biomarkers. Personalized approaches that account for metabolic health could reduce the trial-and-error nature of depression treatment and improve patient outcomes.

Accurately predicting thermophysical properties across various physical states is essential for both industrial and scientific applications. However, experimental data often exhibit variability and noise, requiring robust modeling approaches. In this work, we employ machine learning (ML) techniques to predict methane's thermophysical properties in liquid, vapor, and supercritical phases, including isobaric and isochoric heat capacities, density, specific volume, Joule-Thomson coefficients, enthalpies, sound speed, and shear viscosities, applying an approach recently developed [ACS Eng. Au 5, 226 (2025)10.1021/acsengineeringau.5c00001]. We explore various ML algorithms, including Adaptive Boosting, Bagging, Decision Trees, Extra Trees, Gradient Boosting, Histogram-based Gradient Boosting Regression Tree, K-Nearest Neighbors, Light Gradient Boosting Machine, Nu-Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. The ML models, which use raw experimental data without removing outliers, produce predictions that are closer to those obtained from the equations of state (EOS) employed by the National Institute of Standards and Technology than to the same raw experimental data used as input for developing the EOS. These results highlight ML's potential to identify and generalize complex patterns, smooth inherent noise, and manage the variability of different thermophysical properties. They indicate that ML models, particularly Extra Trees and Gradient Boosting, can offer a scalable alternative for thermophysical property predictions, offering consistency and efficiency over traditional methods. Although our approach does not eliminate preprocessing, it demonstrates that ML can effectively manage noisy data independently, offering a more efficient and cost-effective alternative to conventional pre- and postprocessing techniques.

Sub-Saharan Africa continues to shoulder the heaviest burden of malaria. The 2024 World Health Organisation (WHO) malaria report highlighted that Africa contributed an alarming 94% of the global cases and 95% of the deaths. In the WHO African region, progress towards elimination and management of malaria is hindered by weak health systems and a lack of traditional diagnostic methods such as microscopy and malaria Rapid Diagnostic Tests (mRDTs). The primary aim of this paper is to develop a machine learning (ML) ensemble model for malaria diagnosis using clinical and demographic data, tailored for resource-limited settings. A retrospective study was conducted using 637 patient records from Gutu Mission Hospital and Gweru Provincial Hospital in Zimbabwe. Clinical symptoms (fever, chills, abdominal pain, headache and diarrhea) and demographic features (age, gender, residence and travel history) were analysed. Data preprocessing included handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) and feature selection using Recursive Feature Elimination (RFE). Seven individual ML models, including Logistic regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boosting (GB), K-Nearest Neighbour (KNN), Naive Bayes (NB) and XGBoost were trained and evaluated on the malaria dataset. The individual models were further combined to build, train and evaluate ensemble models such as Bagging, Stacking, Soft Voting and AdaBoost. Model performance was assessed using accuracy, precision, confusion matrices, recall, F1 score, and AUC-ROC metrics. Clinical symptoms (chills: p-value=0.001, fever: p=0.003, diarrhea: p=0.01, abdominal pain: p< 0.001) were statistically significant predictors of malaria. Of the demographic factors, only travel history (p=0.02) showed a significant association with malaria. Among the seven individual ML models, GB achieved the highest predictive performance (Accuracy =0.94), followed by RF (Accuracy =0.94) and XGBoost (Accuracy =0.93). The stacking ensemble model outperformed all individual ML models and other ensemble models (bagging, soft voting and AdaBoost), achieving accuracy =0.96, precision =0.95, recall =0.98, F1 score =0.96 and AUC-ROC =0.98. This study demonstrates that ML, particularly ensemble models, can be used to improve malaria diagnosis significantly. The integration of these models into a web-based application could provide a scalable and accessible diagnostic tool for healthcare workers in resource-limited settings.

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

This study presents a systematic comparison of fourteen supervised classifiers trained to predict binned smartphone addiction levels (Low/Medium/High) in a cohort of 300 teenagers, using demographic, usage, academic, and health related features. After cleaning and binning the continuous Addiction_Level score into three categories, we encoded all categorical variables and standardized inputs, then stratified into 80 % training and 20 % test splits. Our expanded model suite comprised: Logistic Regression, Gaussian Naive Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, Support Vector Machine, and a multilayer perceptron (MLP). Each classifier was evaluated on accuracy, precision, recall, macro-averaged <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score, and multiclass ROC AUC; confusion-matrix entries were flattened into nine “Actual_i to Pred_j” columns per model for granular error analysis. Logistic Regression achieved the highest test accuracy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{ (9 8. 8 3 \%) }$</tex>, outstanding ROC AUC (0.9982) and perfect precision in discriminating the majority class (“High” addiction), despite modest recall for minority classes. MLP followed (96.33 % accuracy, 0.9878 AUC), indicating that a shallow neural network can capture nonlinear patterns but struggles on underrepresented labels. Gradient Boosting, CatBoost, and LightGBM all exceeded 95% accuracy with strong F1-scores (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 0.72-0.73$</tex>) and AUCs above 0.96, demonstrating the power of tree-based ensembles on mixed data types. Simpler methods (e.g., GaussianNB, KNN, Decision Tree) performed moderately (86-91% accuracy, AUC 0.84-0.98), while AdaBoost lagged (77.5 % accuracy, AUC 0.867), suggesting sensitivity to noisy features. Confusion-matrix summaries revealed that most models rarely misclassify Low-addiction teens, but confusion arises between Medium and High classes important for targeted interventions.

In the realm of software development, detecting code smells is a critical task for ensuring good code quality. God class code smell specifically has a specific nature associated with a great deal of subjectivity due to the levels of coupling and cohesion associated to it. Automated techniques for code smell detection aim to resolve this subjectivity. Machine learning techniques have shown promising results that tend to improve accuracy and reduce the bias associated with other techniques for God class identification. This is due to their pattern recognition capabilities making them more objective in identifying patterns that indicate code smells. However, current results need to be further improved in terms of both accuracy and generalizability. The challenge in the use of machine learning is not only in selecting the most appropriate technique but also lies in effectively representing source code as input patterns fed to Machine Learning (ML) classifier(s). Code representation plays a pivotal role in encoding source code for ML algorithms. This study aims improving the accuracy and generalizability of God class code smell detection via exploring the effect of using various code representation techniques, namely, tree-based, metric-based, code embedding, and token-based code representation techniques on the ML detection results. The study is conducted on the MLCQ dataset, and applies various ML algorithms (specifically: Logistic Regression, Random Forest, SVM, Decision Tree, Naive Bayes, Gradient Boosting, XGBoost). The evaluation results show how different code representation techniques influence ML detection outcomes and the comparative performance of ML algorithms. The study findings reveal that the F1-score achieved outperforms prior studies on the MLCQ dataset, indicating the effectiveness of the proposed approach. The presented results reveal how the code representation technique used makes a significant impact on the ML classifier results. This paves the way for developing intelligent IDE plugins for just in time God Class code smell detection among other code smells.

Cardiovascular disease prediction in the field of clinical record diagnosis is difficult. In the healthcare sector, there is a massive volume of data. Machine learning helps individuals make better choices and forecasts by transforming massive amounts of raw information from the healthcare system into realities. The existing study employed a wide range of machine learning based are used to forecast coronary problems including logistic models, decision trees, neural networks, and so on. The proposed system detects coronary heart disease based on boosting methods, a machine learning methodology. The dataset considered for the work is Framingham datasets with 4238 instances and 14 attributes. To improve the system's efficiency, a Feature- Selector optimization model that included a recursive feature elimination and Boruto method choose the best subset of coronary heart disease traits. Then random over-sampling and SMOTE, an extremely effective optimized model-based technique to handle a problem of imbalanced data. For classifications, Random forest, Decision tree, gradient boosting, Adaptive boosting, and Support vector model are performed. The system seems to have an accuracy of 88% for the recursive feature elimination method with the random forest model.

Given the demand for developing the efficient Machine Learning (ML) classification models for healthcare data, and the potentiality of Bio-Inspired Optimization (BIO) algorithms to tackle the problem of high dimensional data, we investigate the range of ML classification models trained with the optimal subset of features of PD data set for efficient PD classification. We used two BIO algorithms, Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO), to determine the optimal subset of features of PD data set. The data set chosen for investigation comprises 756 observations (rows or records) taken over 755 attributes (columns or dimensions or features) from 252 PD patients. We employed MaxAbsolute feature scaling method to normalize the data and one hold cross-validation method to avoid biased results. Accordingly, the data is split in to training and testing set in the ratio of 70% and 30%. Subsequently, we employed GA and BPSO algorithms separately on 11 ML classifiers (Logistic Regression (LR), linear Support Vector Machine (lSVM), radial basis function Support Vector Machine (rSVM), Gaussian Naïve Bayes (GNB), Gaussian Process Classifier (GPC), k-Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Ada Boost (AB) and Quadratic Discriminant Analysis (QDA)), to determine the optimal subset of features (reduction of dimensionality) contributing to the highest classification accuracy. Among all the bio-inspired ML classifiers employed: GA-inspired MLP produced the maximum dimensionality reduction of 52.32% by selecting only 359 features and delivering 85.1% of the classification accuracy; GA-inspired AB delivered the maximum classification accuracy of 90.7% producing the dimensionality reduction of 41.43% by selecting only 441 features; And, BPSO-inspired GNB produced the maximum dimensionality reduction of 47.14% by selecting 396 features and delivering the classification accuracy of 79.3%; BPSOMLP delivered the maximum classification accuracy of 89% and produced 46.48% of the dimensionality reduction by selecting only 403 features.

BACKGROUNDThe International Study Group of Pancreatic Surgery has established the definition and grading system for postpancreatectomy acute pancreatitis (PPAP). There are no established machine learning models for predicting PPAP following pancreaticoduodenectomy (PD).AIMTo explore the predictive model of PPAP, and test its predictive efficacy to guide the clinical work.METHODSClinical data from consecutive patients who underwent PD between 2016 and 2024 were retrospectively collected. An analysis of PPAP risk factors was performed, various machine learning algorithms [logistic regression, random forest, gradient boosting decision tree, extreme gradient boosting, light gradient boosting machine, and category boosting (CatBoost)] were utilized to develop predictive models. Recursive feature elimination was employed to select several variables to achieve the optimal machine algorithm.RESULTSThe study included 381 patients, of whom 88 (23.09%) developed PPAP. PPAP patients exhibited a significantly higher incidence of postoperative pancreatic fistula (55.68% vs 14.68%, P < 0.001), grade C postoperative pancreatic fistula (9.09% vs 1.37%, P = 0.001). The CatBoost algorithm outperformed other algorithms with a mean area under the receiver operating characteristic curve of 0.859 [95% confidence interval (CI): 0.814-0.905] in the training cohort and 0.822 (95%CI: 0.717-0.927) in the testing cohort. According to shapley additive explanations analysis, pancreatic texture, main pancreatic duct diameter, body mass index, estimated blood loss, and surgery time were the most important variables based on recursive feature elimination. The CatBoost algorithm based on selected variables demonstrated superior performance, with an area under the receiver operating characteristic curve of 0.837 (95%CI: 0.788-0.886) in the training cohort and 0.812 (95%CI: 0.697-0.927) in the testing cohort.CONCLUSIONWe developed the first machine learning-based predictive model for PPAP following PD. This predictive model can assist surgeons in anticipating and managing this complication proactively.

Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. The researcher used this algorithm because, in the research that the researcher found on the Kaggle platform, he examined the comparison of the algorithm using the breast cancer dataset. In previous studies, their researchers used SVM, which obtained an accuracy of 96.47%, Neural Networks of 97.06%, and Naïve Bayes with an accuracy of 91.18% to study breast cancer. The difference from previous research is that this study uses several existing algorithms in Machine Learning such as K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. In addition, this research was conducted to see whether the results of the accuracy of the algorithm that the researchers carried out using the lung cancer dataset had different results. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood.

Stress has become one of the mental health adversaries of the COVID-19 pandemic. Several stressors like fear of infection, lockdown, and social distancing are commonly accountable for the stress. The existing stress prediction systems are less compatible to handle diversly changing stressors during COVID-19. The traditional approaches often use incomplete features from limited sources (e.g., only wearable sensor or user device) and static prediction techniques. The Edge Artificial Intelligence (Edge AI) employs machine learning to make data from these sources usable for decision making. Therefore, In this study, we propose a Digital Twin of Mental Stress (DTMS) model that employs IoT-based multimodal sensing and machine learning for mental stress prediction. We obtained 98% accuracy for four widely used Machine Learning(ML) algorithms Naïve Bayes(NB), Random Forest(RF), Multilayer Perceptron(MLP), and Decision Tree (DT). The optimal Digital Twin Features (DTF) could reduce the classification time.