Open Access

ARTICLE

Heart Disease Risk Prediction Expending of Classification Algorithms

Nisha Mary¹, Bilal Khan¹, Abdullah A. Asiri², Fazal Muhammad^3,*, Salman Khan³, Samar Alqhtani⁴, Khlood M. Mehdar⁵, Hanan Talal Halwani⁴, Muhammad Irfan⁶, Khalaf A. Alshamrani²

1 City University of Science and Information Technology, Peshawar, 25000, Pakistan
2 Radiology Science Department College of Applied Medical Science Najran University, Kingdom of Saudi Arabia
3 Department of Electrical Engineering, University of Engineering and Technology, Mardan, 23200, Pakistan
4 Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
5 Anatomy Department, Medicine College, Najran University, Najran, Saudi Arabia
6 Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran, 61441, Saudi Arabia

* Corresponding Author: Fazal Muhammad. Email:

Computers, Materials & Continua 2022, 73(3), 6595-6616. https://doi.org/10.32604/cmc.2022.032384

Received 16 May 2022; Accepted 16 June 2022; Issue published 28 July 2022

Abstract

Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today’s lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease the error rate and enhance the accuracy of HDP assessment measures. However, in order to discover the optimal HDP solution, this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California, Irvine (UCI) machine learning repository. In a comparative analysis, Mean Absolute Error (MAE), Relative Absolute Error (RAE), precision, recall, f-measure, and accuracy are used to evaluate Linear Regression (LR), Decision Tree (J48), Naive Bayes (NB), Artificial Neural Network (ANN), Simple Cart (SC), Bagging, Decision Stump (DS), AdaBoost, Rep Tree (REPT), and Support Vector Machine (SVM). Overall, the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate, with RAE of 33.2631 and MAE of 0.165, the precision of 0.841, recall of 0.835, f-measure of 0.833, and accuracy of 83.49 percent for the dataset gathered from UCI. The SC improves accuracy and reduces the error rate for the Kaggle dataset, which is 3.30% for RAE, 0.016 percent for MAE, 0.984% for precision, 0.984 percent for recall, 0.984 percent for f-measure, and 98.44% for accuracy.

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