An Experimental Analysis of Machine Learning Models for Diabetes Classification

Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection and effective management of diabetes are crucial to prevent severe complications and improve the quality of life for affected individuals. Machine Learning (ML) techniques have shown great promise in aiding the early detection of various diseases on patient data, including diabetes. ML algorithms can analyze vast datasets, identify patterns, and make accurate predictions, helping medical professionals to diagnose diabetes at its early stages. In our work, we employed several ML models for diabetes classification using different datasets. These models include K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient-Boosted Model (GBM), eXtreme Gradient Boosting (XG-Boost), Adaptive Boosting (Ada-Boost), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). We performed a comparative analysis of their performance on three distinct datasets using evaluation metrics like accuracy, precision, F1-score, sensitivity, specificity and Cohen’s Kappa Value. Our findings revealed that the RF algorithm is optimal for symptoms-based and primary lab report-based diabetes detection, while XG-Boost excels in classifying different types of diabetes from a multi-class dataset. Moreover, we investigated diverse symptoms and their impact on diabetes outcomes, offering insights into preventive measures and early stage monitoring for this disease classification.