A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ 2 Statistical Model and Gaussian Naive Bayes.

Liaqat Ali,Shafqat Ullah Khan,Imrana Yakubu,Adeeb Noor,Iqbal Qasim,Noorbakhsh Amiri Golilarz,Redhwan Nour

doi:10.1155/2019/6314328

Abstract

Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ2 statistical model is used to rank the commonly used 13 HF features. Based on the χ2 test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ2-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ2-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%.

Highlights

Heart failure (HF) is a condition whereby the heart is unable to supply enough blood to satisfy the body’s requirements. e coronary artery as an integral part of the heart is accountable for supplying blood to the heart
Ere are many imperilling conditions that result in a HF disease. ese conditions can be put into two categories, with the first category consisting of risk or imperilling conditions that cannot be altered, e.g., patient’s sex, age, and family history. e second category, which can be altered, consists of conditions that are attributed to the way of life of the patient, for instance, smoking habit, high cholesterol level, high level of blood pressure, and physical inactivity [2]
On the basis of χ2 statistical model and Gaussian Naive Bayes (GNB), we proposed a feature-driven decision support system for HF disease prediction

Summary

Introduction

Heart failure (HF) is a condition whereby the heart is unable to supply enough blood to satisfy the body’s requirements. e coronary artery as an integral part of the heart is accountable for supplying blood to the heart. Computational and Mathematical Methods in Medicine disease Some of these tests are electrocardiogram (ECG), nuclear scan, angiography, and echocardiogram [5]. ECG is a noninvasive technique [6, 7] It is not very effective as it may lead to undiagnosed symptoms of HF disease [5]. Is factor leads to angiography, a sort of diagnosis used to verify instances of heart disease. It is considered as the finest approach for HF disease diagnosis. It is necessary to develop an efficient, intelligent, medical decision-making support system with the principles of data mining and machine learning

Methods

Results

Conclusion