A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

Abstract Traditional diagnostic techniques are prone to human error and are time-consuming. Computer-aided diagnostic procedures improve performance and reduce expenses. This paper presents machine learning-based classifiers to detect diabetes in India, and Indian Demographic & Health Survey (2019–21) dataset is considered for the analysis. Classifiers like Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and Random Forest are considered. The four phases adopted in work include preprocessing, feature extraction, classification, and analysis. Principal Component Analysis is used for feature reduction. Hyper-tuning parameters are tuned to achieve good performance of the classifiers. From the results, Random Forest has given the maximum classification accuracy, precision, recall, and area under the curve compared with other models. The work finds application in healthcare for the predictive analysis of diabetes.