Optuna-Optimized Machine Learning Technique for Accurate Diabetes Prediction and Classification

Diabetes is a life-threatening chronic condition af-fecting millions worldwide., making timely prediction and risk classification crucial for effective intervention. Various machine learning techniques., such as deep learning., decision trees., random forests., and support vector machines (SVM)., have been applied to this task., but they face significant challenges. These challenges in-clude handling imbalanced datasets., optimizing hyperparameters., preventing overfitting., and maintaining high accuracy while en-suring interpretability. Additionally., adapting models for person-alized treatment based on clinical and demographic data requires precise hyperparameter tuning., which can be time-consuming and complex. This study addresses these challenges by introducing an Optuna optimization framework for diabetes prediction and clas-sification. Optuna is a powerful hyperparameter optimization tool that efficiently explores the parameter space to identify optimal configurations for improved model performance. Using a compre-hensive dataset of diabetic patients., we apply machine learning models such as logistic regression., SVM., and random forests., which are fine-tuned through Optuna's automatic tuning process. The results demonstrate significant improvements in accuracy., precision., recall., and Fl-score over conventional methods., leading to better diabetes prediction and risk stratification. This study highlights the potential of Optuna to enhance machine learning models., improving their applicability in clinical settings.