Analysis of Hypertension Risk Factors in Urban Adolescents Using the Rapidminer Data Mining Method with Decision Tree C4.5 Operator
Abstract
Hypertension in adolescents is an increasing health problem due to lifestyle changes. This study aims to identify risk factors for hypertension in urban adolescents using the data mining method with the Decision Tree C4.5 algorithm on RapidMiner. This quantitative research involved 130 adolescents aged 15–19 years in the working area of the Dumbo Raya Health Center, Gorontalo City. The variables analyzed included genetic factors, obesity, stress, smoking, excessive salt consumption, coffee consumption, sleep duration, and hypertension status. The data was processed through the preprocessing stage, the formation of the C4.5 Decision Tree model, and evaluation using a confusion matrix with an 80:20 training and testing data division. The results showed that sleep duration was the most dominant factor, followed by coffee consumption, genetic factors, obesity, excessive salt consumption, smoking, and stress. The model produces an accuracy of 88.46% and is able to form easy-to-understand classification rules. The Decision Tree C4.5 algorithm is effectively used to support early detection of hypertension in adolescents.
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