Comparison of machine learning algorithms for diagnosing diabetes

Ch. A. Alimbayev, Zhadyra N. Alimbayeva, Zh. S. Orynbay, Кassymbek Ozhikenov, Yerbolat Igembay

2024

Full text Open access: Gold Metadata (CSL-JSON)

DOI 10.47533/2024.1606-146x.46

Data confidence 70 medium

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Abstract

This study compares various machine learning algorithms for detecting diabetes in patients. The data used included biometric parameters such as blood glucose, blood pressure, body mass index (BMI), and other indicators. Several models were tested, including logistic regression, support vector machine (SVM), k-nearest neighbors (KNN), and ensemble methods such as XGBoost, LightGBM, and CatBoost. Class balancing methods using SMOTE and hyperparameter optimization using GridSearchCV were used to improve the accuracy of the models. The XGBoost model demonstrated the highest prediction accuracy of 90.2%. These results confirm the high efficiency of machine learning algorithms in diagnosing diabetes, which can be used in medical practice to create more accurate prediction tools.