Abstract :

 Diabetes is a chronic health condition affecting millions globally, causing severe complications and burdening healthcare systems. Current machine learning methods for diabetes prediction face challenges such as data imbalance, limited generalizability, and computational inefficiency. This study proposes a novel method that combines K-Nearest Neighbors (KNN), clustering techniques, Synthetic Minority Over- sampling Technique (SMOTE), and Random Forest for outcome classification to address these issues. The PIMA Indian Diabetes Dataset was used to evaluate the approach, achieving accuracy of 87.50%. However, the study has limitations, such as dependency on specific datasets and computational complexity. Future work will focus on validating the method across diverse datasets, optimizing computational efficiency, and developing real-time prediction capabilities.

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Keywords :

Data Imbalance, Diabetes Prediction, Healthcare, Machine learning, Random forest, Synthetic Minority Over-sampling Technique.

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