Diabetes type 2 classification using machine learning algorithms with up-sampling technique

Abstract

Recently, the rate of chronic diabetes disease has increased extensively. Diabetes increases blood sugar and other problems like blurred vision, kidney failure, nerve problems, and stroke. Researchers for predicting diabetes have constructed various models. In this paper, gradient boosting classifier, AdaBoost classifier, decision tree classifier, and extra trees classifier machine learning models have been utilized for identifying chronic diabetes disease. The models analyze the PIMA Indian Diabetes dataset (PIMA) and Behavioral Risk Factor Surveillance System (BRFSS) diabetes datasets to classify patients with positive or negative diagnoses. 80% of the datasets are used as training data and 20% as testing data. The extra trees classifier with an area under curve of 0.96% for PIMA and 0.99% for BRFSS datasets outperformed other models. Therefore, it is suggested that healthcare providers can use the ETC model to predict chronic disease.