Comparison of Machine Learning Methods for a Diabetes Prediction Information System

Abstract

Diabetes is a disease for which there is no permanent cure; therefore, methods and information systems are required for its early detection. This paper proposes an information system for predicting diabetes based on the use of data mining methods and machine learning (ML) algorithms. The paper discusses a number of machine learning methods such as decision trees (DT), logistic regression (LR), k-Nearest Neighbors (k-NN). For our research, we used the Pima Indian Diabetes (PID) dataset collected from the UCI machine learning repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. Research has been carried out to improve the prediction index based on the Recursive Feature Elimination method. We found that the logistic regression (LR) model performed well in predicting diabetes. We have shown that in order to use the created model to predict the likelihood of diabetes mellitus with an accuracy of 78%, it is necessary and sufficient to use such indicators of the patient's health status as the number of times of pregnancy, the concentration of glucose in the blood plasma during the oral glucose tolerance test, the BMI index and the result of the calculation. heredity functions "DiabetesPedigreeFunction".