DETERMINING AFFECTING FACTORS OF HYPERTENSION WITH DATA MINING TECHNIQUES

Abstract

In this paper, Age, Gender, Body Mass Index, HDL, LDL, Triglyceride, Uric Acid and The Use of Smoking data gathered from 150 patients are analyzed with data mining classification algorithms. The data is divided into two different classes which are normal and patient. Thus, a diagnostic system is developed which predicts whether a candidate patient has hypertension or not. Besides, a decision tree is created and factors affecting hypertension directly and indirectly are determined. In this study, C4.5, Naive Bayes and Multilayer perceptron classification algorithms are used, and shown that C4.5 algorithm gives better results.