Enhancing a Diabetes Prediction-Aided System : The Impact of SMOTE

Abstract

New public health issue (COVID-19) was beginning to emerge in the latter part of 2019 with a risk that specifically affected diabetics. The use of algorithm- based methods and artificial intelligence to help clinical decision-making is becoming increasingly important. Unaware of their diabetes, one in two people live with it. They are not being kept in check or having their condition monitored. Therefore, if they contract the coronavirus, they are more susceptible to have new complica-tions. Early identification is crucial for the management and treatment of diabetes because this disease will have a lengthy asymptomatic phase. This article is an experiment with the use of machine learning models for categorizing early-stage diabetes for a performant system. The most recent dataset, dated July 22, 2020, is being used. 3 pertinent machine learning algorithms have been used to analyze the dataset