Earlier detection of individuals at the highest risk of developing diabetes is crucial to avoid the disease's prevalence and progression. Therefore, we aim to build a data-driven predictive application for screening subjects at a high risk of developing Type 2 Diabetes mellitus (T2DM) in the western region of Saudi Arabia. In this context, we designed and implemented a questionnairebased cross-sectional study using conventional diabetes risk factors for studying the prevalence and the association between the outcomes and exposure (s). We used the Chi-Squared test and binary logistic regression to analyze and screen the most significant diabetes risk factor for T2DM risk prediction. Synthetic Minority Over-sampling Technique (SMOTE), a class-balancer, was used to balance the cross-sectional data. We used the balanced class data to screen the best performing classification algorithm to classify patients at high risk of diabetes with a higher F1 Score. The best performing classifier's hyper-parameters were further tuned using 10-fold cross-validation for achieving an improved F1 Score.Additionally, we validated our proposed model with the existing models built using the National Health and Nutrition Examination Survey (NHANES) dataset and Pima Indian Diabetes (PID) dataset. The results of the Chi-squared test and binary logistic regression showed that the exposures, namely Smoking, Healthy diet, Blood-Pressure (BP), Body Mass Index (BMI), Gender, and Region, contributed significantly (p < 0.05) to the prediction of the Response variable (subjects at high risk of diabetes). The tuned two-class Decision Forest (DF) model showed better performance with an average F1score of 0.8453 ± 0.0268. Moreover, the DF based model adapted reasonably well in different diabetes dataset. An Application Programming Interface (API) of the tuned DF model was implemented and deployed as a web service at https://type2-diabetes-riskpredictor.herokuapp.com, and the implementation codes are available at https://github.com/SAH-ML/T2DM-Risk-Predictor.

The fact that ensemble methods enhance the prediction performance. Therefore, we focused on developing a weighted ensemble method using a novel combination of Cerebrospinal Fluid (CSF) protein biomarkers to predict AD's earlier stages with greater accuracy than the stateof-the-art CSF protein biomarkers. In this regard, two feature selection methods, namely the Recursive Feature Elimination (RFE) and L1 regularization method were used to screen the most important subset of features for building a classification model using the Mild Cognitive Impairment (MCI) dataset. A novel combination of three biomarkers, namely Cystatin C, Matrix metalloproteinases (MMP10), and tau protein, was screened using the linear Support Vector Machine (SVM) and Logistic Regression (LR) classifier based RFE method. Two-tailed unpaired t-test analysis at a 5 % significance level showed a significant difference between the mean levels of Cystatin C, MMP10, and tau protein between cognitive normal and cognitively impaired groups. An ensemble model using a weighted average of two best performing classifiers (LR and Linear SVM) was created using a novel subset of three most informative features. Our ensemble model's weighted average results performed significantly better than LR and Linear SVM base classifiers' performance. The Receiver Operating Characteristic Curve (ROC_AUC) and Area under Precision-Recall values (AUPR) of our proposed model were observed to be 0.9799 ± 0.055 0.9108 ± 0.015, respectively. The performance of our proposed weighted averaged ensemble model built using a novel combination of CSF protein biomarkers was significantly better (p < 0.001) than models generated using different combinations of CSF protein biomarkers obtained from recent studies. An ensemble-learning based application was implemented and deployed at Heroku at https://appsalzheimer.herokuapp.com.

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

Parkinson’s disease (PD) currently affects approximately 10 million people worldwide. The detection of PD positive subjects is vital in terms of disease prognostics, diagnostics, management and treatment. Different types of early symptoms, such as speech impairment and changes in writing, are associated with Parkinson disease. To classify potential patients of PD, many researchers used machine learning algorithms in various datasets related to this disease. In our research, we study the dataset of the PD vocal impairment feature, which is an imbalanced dataset. We propose comparative performance evaluation using various decision tree ensemble methods, with or without oversampling techniques. In addition, we compare the performance of classifiers with different sizes of ensembles and various ratios of the minority class and the majority class with oversampling and undersampling. Finally, we combine feature selection with best-performing ensemble classifiers. The result shows that AdaBoost, random forest, and decision tree developed for the RUSBoost imbalanced dataset perform well in performance metrics such as precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC) and the geometric mean. Further, feature selection methods, namely lasso and information gain, were used to screen the 10 best features using the best ensemble classifiers. AdaBoost with information gain feature selection method is the best performing ensemble method with an F1-score of 0.903.

The resistance to delamination in polymer composite depends on their constituents, manufacturing process, environmental factors, specimen geometry, and loading conditions. The manufacturing of laminated composites is usually carried out at an elevated temperature, which induces thermal stresses in composites mainly due to a mismatch in the coefficient of thermal expansion (CTE) of fiber and matrix. This work aims to investigate the effect of these process-induced stresses on mode-I interlaminar fracture toughness (GI) of Glass-Carbon-Epoxy (GCE) and Glass-Epoxy (GE) composites. These composites are prepared using a manual layup technique and cured under room temperature, followed by post-curing using different curing conditions. Double cantilever beam (DCB) specimens were used to determine GI experimentally. The slitting technique was used to estimate residual stresses (longitudinal and transverse direction of crack growth) inherited in cured composites and the impact of these stresses on GI was investigated. Delaminated surfaces of composites were examined using a scanning electron microscopy (SEM) to investigate the effect of post-curing on the mode-I failure mechanism. It was found that GI of both GE and GEC composites are sensitive to the state of residual stress in the laminas. The increase in the GI of laminates can also be attributed to an increase in matrix deformation and fiber–matrix interfacial bond with the increase in post-curing temperature.