Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm
Aga Maulana, Farassa Rani Faisal, Teuku Rizky Noviandy, Tatsa Rizkia, Ghazi Mauer Idroes, Trina Ekawati Tallei, Mohamed El‐Shazly, Rinaldi Idroes
Abstract
Diabetes is a chronic condition characterized by elevated blood glucose levels which leads to organ dysfunction and an increased risk of premature death. The global prevalence of diabetes has been rising, necessitating an accurate and timely diagnosis to achieve the most effective management. Recent advancements in the field of machine learning have opened new possibilities for improving diabetes detection and management. In this study, we propose a fine-tuned XGBoost model for diabetes detection. We use the Pima Indian Diabetes dataset and employ a random search for hyperparameter tuning. The fine-tuned XGBoost model is compared with six other popular machine learning models and achieves the highest performance in accuracy, precision, sensitivity, and F1-score. This study demonstrates the potential of the fine-tuned XGBoost model as a robust and efficient tool for diabetes detection. The insights of this study advance medical diagnostics for efficient and personalized management of diabetes.