Hypertension Prediction System Using Naive Bayes Classifier

Abstract

Hypertension is an illness that often leads to severe and life-threatening diseases such as heart failure, coronary artery disease, heart attack and other severe conditions if not promptly diagnosed and treated. Data Mining the use of a variety of techniques to smoothen information discovery or decision-making knowledge in the database and extracting these in a way that they can put to use in areas such as predictions, forecasting and estimation. This research has developed hypertension predictive system using data mining modelling technique, namely, Naïve Bayes. Medical profiles such as age, sex, blood pressure, chest pain and blood sugar it can predict the likelihood of patients getting a hypertension. This work was implemented in WEKA environment as an application which takes medical test’s parameter as an input. The 10-fold cross validation method was used to train the developed predictive model and the performance of the models evaluated. This paper presents a model for predicting hypertension with 83.69%. The naïve Bayes’ classifier proved to be an effective algorithm for predicting the diagnosis of hypertension in Nigerian patients. It can serve a training tool to train nurses and medical students to diagnose patients with hypertension.