Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Ashir Javeed,Sanam Shahla Rizvi,Shafqat Ullah Khan,Rabia Riaz,Shijie Zhou,Se Jin Kwon

doi:10.1155/2020/8843115

Abstract

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

Highlights

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis
Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. e strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve
Various risk factors are identified that cause the heart disease. e risk factors of heart diseases are classified into two major types such as the risk factors that can alter, e.g., smoking and physical exercise, and the risk factors that cannot alter, e.g., gender, age, and patient’s family history [1]. e diagnosis of heart through conventional medical methods is quite difficult, complex, time consuming, and costly. erefore, the diagnosis of heart disease is worst in developing countries due to lack of state-of-the-art examination tools and medical experts [2, 3]

Summary

Introduction

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. Boonijing proposed a classification technique based on multilayer perceptron (MLP) in addition to backpropagation learning algorithm and biomedical test values for diagnosing the heart disease through a feature selection algorithm. E proposed method consists of jeopardizing factor identifiers adopting a correlation based subset (CFS) selection with particle swarm optimization (PSO) search model and K-means Supervised learning algorithms such as multilayer perceptron (MLP), multinomial logistic regression (MLR), fuzzy unordered rule induction algorithm (FURIA), and C4.5 are utilized to design CAD cases. It was studied that the best performance of the data mining technique for classification accuracy was 87.4% for the heart disease prediction. Samuel et al proposed a diagnostic system developed from ANN and Fuzzy AHP. e prediction accuracy of 91.10% was reported from the ANN and Fuzzy AHP diagnosis system [4]

Methods

Results

Conclusion