Identification of Diabetes Mellitus Risk Factors With a Data Mining Classification Approach - Ade Agustina; Galih Ady Permana; Christina Juliane (Indonesian Journal of Artificial Intelligence and Data Mining, 2022)
Diabetes mellitus is a chronic disease characterized by an increase in the frequency of eating, drinking and urinating due to the failure of the process of sugar entering the body to be converted into energy due to the pancreas function not being able to produce enough insulin or not producing insulin at all. The purpose of writing this paper is to test the accuracy of the decision tree and rules generated by the ID3 algorithm and correlate it with literature studies from research that has been carried out by researchers in the health sector related to diabetes and the results of this classification are expected to be used as a reference. For everyone to be able to change their lifestyle to avoid the risk of developing diabetes mellitus by looking at the attributes of the dataset. In this study, the application of data mining with the classification method with the ID3 algorithm using datasets from the BRFSS survey results was carried out. The results of data testing can be obtained from the accuracy of the rules generated by the ID3 algorithm with an accuracy rate of 85.95%. The rules generated by the ID3 algorithm are also correlated with the literature from research that has been carried out by researchers in the health sector, and the results are that the rules generated from the attribute indicators of the dataset have relevance and suitability