Rule-based prediction of diabetes mellitus using a classification based on association rules

Abstract

Diabetes mellitus, a chronic metabolic disease, is characterised by persistently high blood sugar levels. It is projected that by 2030, the number of individuals with diabetes in developing nations would rise from roughly 84 million to 228 million, placing a substantial strain on healthcare systems. Therefore, there is a need for different predictions that can be used in early diagnosis, follow-up and preventive medicine for this disease. In this study, a data mining algorithm, the association classification approach, is used to classify diabetes on an open source dataset. The performance metrics of the model are accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1-score values of 0.92, 0.78, 0.58, 0.98, 0.85, 0.93, 0.70 respectively. According to these results, the classification model based on association rules is highly successful in classifying diabetes melitus. In addition, as an output of the model, certain rules are proposed that can be used in early diagnosis, treatment and preventive medicine of diabetes mellitus.