AI Summary & Key Takeaways
AI InsightThe results demonstrate that the voting classifier exhibits highly balanced performance and achieves the highest scores among the compared models in terms of accuracy, precision, specificity, F1 score, and MCC metrics.
📌 3 Key Takeaways
Abstract
The increasing global prevalence of diabetes mellitus poses severe public health challenges, underscoring the vital need for prompt and accurate diagnostic strategies. In this study, machine learning and deep learning-based models were individually evaluated for diabetes prediction, and the best-performing models were subsequently combined using a voting classifier structure. The dataset used was obtained from Kaggle, and preprocessing steps included the removal of missing values and outliers (resulting in the removal of 35,828 records, or 35.8% of the initial data). Statistical analysis showed that none of the continuous variables followed a normal distribution, and Mann-Whitney U tests revealed statistically significant differences between diabetic and non-diabetic groups. Five machine learning algorithms—Random Forest, XGBoost, Naive Bayes, Logistic Regression, and Support Vector Machine—and two deep learning models—Multilayer Perceptron and TabNet—were assessed based on accuracy, precision, recall, and F1-score. The top-performing models (RF, XGBoost, MLP, and TabNet) were integrated using a soft voting approach. The results demonstrate that the voting classifier exhibits highly balanced performance and achieves the highest scores among the compared models in terms of accuracy, precision, specificity, F1 score, and MCC metrics. However, it is notable that for the Recall metric-which is clinically critical for primary disease screening- the individual MLP (0.7000) and XGBoost (0.6964) models outperformed the ensemble structure. These findings emphasise that ensemble learning structures, which combine the strengths of multiple models, can provide more reliable decision support in critical areas such as healthcare. For future research, the generalizability of the model can be enhanced by using datasets with a broader range of features, and real-time adaptive systems supported by cloud computing technologies can be developed to improve the responsiveness and efficiency of diabetes prediction models.