Jul 2026· Journal of Science Research and Reviews· Vol 3, pp. 207-214· 0 citations
TL;DR
This study investigated the application of machine learning techniques and exploratory data analysis to forecast early-onset of diabetes using the PIMA Indian dataset, finding promising results.
Abstract
Diabetes is one of the leading causes of morbidity and mortality worldwide. To avoid difficult management of the condition, there is need to predict early onset of the condition. This study investigated the application of machine learning techniques and exploratory data analysis to forecast early-onset of diabetes using the PIMA Indian dataset. Preprocessing included handling missing values and standardization, leading to the development and evaluation of 5 models which include Logistic Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, and Neural Network. Exploratory analysis identified glucose concentration, body mass index, and age as the most influential features. Random Forest achieved the highest accuracy (0.74%) while both Random Forest and Logistic Regression attained the best ROC-AUC score of 0.81%. Feature importance analysis emphasized the predictive significance of glucose and BMI, aligning with clinical knowledge of diabetes risk factors. Despite the promising results the study acknowledged limitations related to the PIMA dataset's demographic scope and the moderate complexity of neural networks, highlighting areas for future enhancement. Ethical considerations, including data privacy and algorithmic bias, were addressed to ensure responsible model development.
It is recommended that healthcare systems adopt XGBoost-based predictive models in clinical decision support tools for early screening, while future studies should validate these models using real-world clinical data to enhance reliability and generalizability.
Idehen Emmanuel Imafidon, Chikere Obinna Munachiso, Dominic Evans Onyebuchi et al.· International Journal of Sci...· 0 citations
Diabetes mellitus is a chronic metabolic disorder whose global prevalence continues to rise, creating an urgent need for scalable, low-cost tools for early risk identification. This study evaluates the effectiveness of five machine learning algorithms (Random Forest, XGBoost, Support Vector Machine [SVM], CatBoost, and TabNet) for predicting diabetes risk from routinely available clinical and lifestyle variables. Using the Pima Indians Diabetes dataset, a preprocessing pipeline was applied that included median imputation of physiologically implausible zero values, standardized (Z-score) feature scaling, Boruta-based feature selection, and class-imbalance handling through class-weight adjustment and the Synthetic Minority Oversampling Technique (SMOTE). Models were trained on an 80/20 train-test split and assessed using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC-AUC). Random Forest achieved the strongest overall performance (accuracy 0.753; F1-score 0.689; ROC-AUC 0.810), followed by CatBoost, SVM, and XGBoost, whereas TabNet performed worst with very low recall for the diabetic class. The best-performing model (Random Forest) was deployed in a lightweight Flask web application that returns a probability-based diabetes risk assessment, categorising each prediction as low, moderate, or high risk together with a tailored recommendation. The findings confirm that ensemble tree-based methods, particularly Random Forest, provide a reliable, interpretable, and deployable basis for diabetes risk screening, especially in resource-constrained settings. Key limitations include dataset homogeneity, residual class imbalance, and limited feature coverage.
T. Olayinka· FUDMA Journal of Sciences· 0 citations
Stroke is a leading cause of death and disability worldwide, and early risk prediction is essential for reducing its public health burden. In this paper, six machine learning models were developed and thoroughly compared. An analysis of stroke risk using the publicly available Kaggle Stroke data set. The article discusses the Prediction Dataset and the use of Logistic Regression, Random Forest, and Support Vector methods. The performance of Machine, K-Nearest Neighbors, Decision Tree, and XGBoost was assessed by means of the Accuracy, Precision, Recall, F1-score, and AUC-ROC metrics. Because class weighting was used to deal with the severe data imbalance, it was found that. Since Logistic Regression, Random Forest, and XGBoost all gave AUC-ROC values above 0.80, they therefore meet the clinically acceptable threshold for effectiveness. Since regression gave the highest Recall of 0.84, it is clear that regression is superior. The performance of identifying high-risk patients is discussed in connection with feature importance. From the analysis it was clearly established that age, average glucose level, and body mass index were the most important predictors, hence the study properly validates this. The paper discusses the feasibility of using machine learning for stroke risk prediction and therefore gives a very useful reference for clinical auxiliary screening.
The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare and underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations
Abstract. Early prediction of type 2 diabetes mellitus is important to facilitate faster and more accurate diagnosis and clinical decision-making. This study aims to compare the performance of the Random Forest and Support Vector Machine (SVM) algorithms in predicting diabetes and to analyze the effect of applying the Synthetic Minority Over-sampling Technique (SMOTE) to imbalanced data. The study used the Bangladesh Diabetes 2025 dataset, following the stages of data selection, preprocessing, data transformation, modeling, and evaluation. The preprocessing stage included median imputation and the removal of duplicate data, while feature standardization was performed after data splitting to prevent data leakage. The dataset was split using an 80:20 ratio with a stratification technique. The study applied two experimental scenarios: one without SMOTE and one with SMOTE applied only to the training data. Model evaluation was conducted using the metrics accuracy, precision, recall, and F1-score based on the test data. The results show that Random Forest outperforms SVM. In the scenario without SMOTE, Random Forest achieved an accuracy of 94.8%, precision of 96.4%, recall of 97.0%, and an F1-score of 96.7%, while SVM achieved an accuracy of 91.5% and an F1-score of 94.6%. After applying SMOTE, Random Forest’s performance improved slightly to an accuracy of 95.3% and an F1-score of 97.0%, while SVM’s performance declined to an accuracy of 90.6% and an F1-score of 93.9%. The results of the study show that Random Forest is the best model for predicting type 2 diabetes mellitus in the dataset used.
Baharudin Yusuf· Jurnal Informatika dan Tekni...· 0 citations
Diabetes affects over 101 million people in India, with many more at risk due to routine and hereditary factors. Early diagnosis is crucial to prevent complications, which make accurate predictive tools essential in healthcare. This research uses Machine Learning (ML) algorithms to evaluate the likelihood of Type 2 Diabetes Mellitus (T2DM) using lifestyle and family history data. The trained models demonstrate strong predictive ability, allowing individuals to self-assess their risk and supporting healthcare professionals in early detection and intervention. This study presents a performance assessment of seven ML classifiers: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (k-NN), and Extreme Gradient Boosting (XGBoost). These classifiers were applied to the widely used PIMA Indian Diabetes dataset (PIDD), which contains 768 clinical records of adult women aged 21 and above, providing key medical information for diabetes analysis. Multiple evaluation measures were applied to assess model performance with results showing that SVM achieved the highest accuracy and AUC, while LR, RF, and XGBoost also performed competitively. Although k-NN attained the highest recall, it yielded a higher false positive rate. These findings highlight that no single model is perfect for every situation, and the choice of classifier should match clinical needs. This study serves as a reference for ML applications in diabetes prediction.
Rizwan Akhtar, Muhammad Kalamuddin Ahamad· ITEGAM- Journal of Engineeri...· 0 citations
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