An AI-Driven Framework for Diabetes Prediction and Classification Using Medical Data, SMOTE, and Grid Search CV Optimization
DOI:
https://doi.org/10.70112/ajeat-2026.15.2.4370Keywords:
Supervised Machine Learning, Classification Task, Algorithms, SMOTE, Grid Search CV, Diabetes PredictionAbstract
In India, approximately 101 million people are diabetic, while around 136 million are prediabetic, making early diagnosis essential for preventing severe health complications. The ML field of AI can emerge as an alternative for diagnosing diabetes. In this study, various algorithms are employed for diabetes prediction using the Pima Indian Diabetes Dataset. Hyperparameter optimization was performed using Grid Search Cross Validation (Grid Search CV) to identify the optimal model parameters. Since the dataset is imbalanced, experiments were conducted on both the original imbalanced dataset and a balanced synthesized dataset generated using SMOTE, which significantly reduced False Negative (FN) predictions across most algorithms. Furthermore, Random Forest and Naive Bayes achieved the highest recall of 72.73% after applying SMOTE, demonstrating improved effectiveness in identifying diabetic patients.
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