Enhancing Diabetes Prediction With Minimal Processing Time Using Catboost: A Comparative Study.

Abstract

Objective: Diabetes mellitus is a chronic disease that presents significant health challenges worldwide. Accurate diabetes prediction facilitates early intervention and personalized healthcare strategies, thereby improving patient care and reducing healthcare processing costs. Ensemble-based machine learning (ML) methods enhance predictive performance.

Method: This study explores various ML classifiers, both individually and in ensemble configurations, including decision trees, random forests, k-nearest neighbors, Naive Bayes, AdaBoost (AB), XGBoost (XB), and multilayer perceptron (MLP) for prediction. The performance of each method is evaluated through rigorous experimentation and comparative analysis across multiple aspects.

Results: The performance of the best ML model, MLP, is compared with that of the proposed CatBoost classifier and the ensemble model to identify the most effective approach for diabetes prediction in minimal duration. The proposed CatBoost classifier's execution time of 4.27 s, which is approximately 98.64% faster than the ensemble model's 314.96 s. This demonstrates CatBoost's significant advantage in computational efficiency over ensemble-based classifiers.

Conclusion: By leveraging the diverse and complementary strengths of ML classifiers, this study contributes to the advancement of precision medicine and personalized healthcare for individuals at risk of diabetes.