Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test.

Alabi Waheed Banjoko,Kawthar Opeyemi Abdulazeez

doi:10.21315/mjms2021.28.5.12

Abstract

BackgroundThe computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm.MethodsThe algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed.ResultsThe misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered.ConclusionFinally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.

Highlights

Heart disease is considered a lifethreatening illness because the heart is a vital organ [1]
The response variable is based on the result of the coronary angiographic test performed on each patient, with yi = 1 indicating the presence of heart disease and yi = −1 indicating the absence of heart disease
Based on the report of Banjoko et al [17], the radial basis function (RBF) kernel is adopted for the w-support vector machine (SVM) algorithm on the heart disease data employed here

Summary

Introduction

Heart disease is considered a lifethreatening illness because the heart is a vital organ [1]. The diagnosis of heart disease is usually based on symptoms, a physical assessment and a medical evaluation, such as the coronary angiographic test [2]. Several different organisations have made recommendations regarding the optimal approach for identifying coronary heart disease in a patient in nonemergency settings [3]. Several factors, such as smoking, level of cholesterol, obesity, hereditary issues and others, have been reportedly associated with heart disease [4]. These factors, have different levels of association with heart disease and each is likely to be more pronounced in the angiographic diagnostic process of the patient. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm

Methods

Results

Discussion

Conclusion