Performance Comparison of Random Forest and XGBoost for Diabetes Prediction

Author(s)	Dr. Vijay Kumar Samyal, Aditya Kumar
Country	India
Abstract	Machine learning is widely used in modern healthcare systems to support disease prediction and diagnosis. Diabetes is one of the most common chronic diseases, and early prediction is important to reduce long-term risks. This paper compares two popular machine learning models, Random Forest and XGBoost, for diabetes prediction. A publicly available diabetes dataset is used, and the models are evaluated using accuracy, precision, recall, and F1-score. Experimental results show that XGBoost performs slightly better in accuracy and precision, while Random Forest performs competitively with simpler tuning and faster execution. The study concludes that both models are effective, but XGBoost provides better overall performance for healthcare prediction.
Keywords	Machine Learning, Diabetes Prediction, Random Forest, XGBoost, Healthcare, Classification, Accuracy.
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-14
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9878
Short DOI	https://doi.org/hbf82z

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