Enhancing Machine Learning-based Model for Early Detection of Diabetes

This research aims to develop machine learning (ML) models for the early identification of diabetes, a chronic condition that presents considerable global health threats, including heart disease, kidney failure, and neuropathy. Conventional diagnostic techniques, such as fasting plasma glucose (FPG) and HbA1c tests, tend to be invasive and often recognize the illness only in its advanced stages. To tackle this issue, the study investigates a range of ML algorithms, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, AdaBoost, Naive Bayes, and XGBoost. It also incorporates comprehensive data preprocessing methods like feature scaling and Synthetic Minority Over-sampling (SMOTE). The goal of these initiatives is to improve the models’ performance and interpretability to facilitate earlier diagnosis. Among the models assessed, K-Nearest Neighbors (KNN) and AdaBoost stood out, both achieving an accuracy of 0.7727, with XGBoost following closely at 0.7662. After tuning, Support Vector Machines (SVM) showed a significant improvement, reaching an accuracy of 0.85, while KNN achieved an accuracy of 0.87 on the test set. Although Decision Trees and Naive Bayes performed less effectively, this research highlights the necessity of model interpretability in clinical settings. Techniques such as SHapley Additive exPlanations (SHAP) are utilized to elucidate predictions. The implementation of these models on a web-based platform using flask framework which allows for real-time assessments of diabetes risk and insights into critical risk factors, aiding healthcare providers and patients in making better-informed decisions.