Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm

Abstract

This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertension from experienced cardiologists, structured questionnaires were used to collect information about the risk factors and the associated risk of hypertension from selected respondents. The predictive model was formulated using two (2) decision trees algorithms, namely: C4.5 and ID3 based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The results revealed that the decision trees algorithms selected some risk factors among those identified as most predictive for the risk of hypertension based on the information inferred from the dataset collected. The variables were used by the decision trees algorithm to deduce the decision trees that were used to infer the risk of hypertension based on the values of the identified risk factors. The ID3 with an accuracy of 100% outperformed the C4.5 which showed an accuracy of 86.36%. The variables identified by the algorithms can help assist cardiologists concentrate on a smaller yet important set of risk factors for identifying the risk of hypertension using rules derived from the path along the decision trees based on the value of the risk factors of the individual.