Optimising hyperparameters with a tree structured Parzen estimator to improve diabetes prediction

Abstract Diabetes is a lifelong condition that occurs when the pancreas loses its ability to secrete insulin or experiences a significant reduction in insulin production. Early identification of high-risk patients is crucial for timely interventions and improved outcomes. Traditional clinical risk prediction models rely on regression analysis using clinical, sociodemographic, and anthropometric data; however, they have limitations in terms of accuracy and generalizability. This research proposes a diagnostic strategy leveraging machine learning (ML) techniques, specifically the XGBoost algorithm optimised with Optuna, to enhance high-risk prediction based on laboratory parameters. The study utilises an open-access diabetes dataset incorporating patient demographics, laboratory test results, and clinical outcomes. Data preprocessing, including cleaning, normalisation, and feature extraction, is performed using an Adaptive Tree-Structured Parzen Estimator (ATPE) and XGBoost model. The proposed model outperforms conventional classification models, achieving 83% accuracy, 80% precision, 78% recall, and a 78% F1 score. A comprehensive correlation and confusion matrix evaluation highlights the model’s effectiveness in distinguishing high-risk patients. Findings indicate that integrating machine learning (ML)-based risk classification frameworks with laboratory test-based diagnostic strategies improves predictive accuracy and patient stratification. However, data quality, population diversity, and real-time applicability remain challenges. Future research should explore the integration of real-time data from wearable devices and expand model deployment to other chronic and rare diseases, enhancing adaptability and clinical decision-making.