Title: Triple voting: hybrid cardiovascular diseases prediction model

Authors: Dahlak Daniel Solomon; Karan Kumar; Sonia; Kushal Kanwar; Kemal Polat

Addresses: Yogananda School of AI Computers and Data Science, Shoolini University, Solan, Himachal Pradesh, India ' Electronics and Communication Engineering Department, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala – 133207, India ' Yogananda School of AI Computers and Data Science, Shoolini University, Solan, Himachal Pradesh, India ' Department of Computer Science and Engineering and Information Technology, Jaypee University of Information and Technology, Solan, Himachal Pradish, India ' Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey

Abstract: Currently, cardiovascular diseases are a high-risk cause of death in both developed and developing countries. Thus, heart disease prognosis has received substantial interest in the medical field worldwide. The incidence of heart disorders is escalating at an alarming rate, and it is crucial and worrisome to anticipate their occurrence. Predicting and detecting cardiovascular disease using machine learning and data mining might be clinically useful, but difficult. There are numerous machine learning algorithms accessible, several studies have developed machine learning algorithms for early cardiac disease prediction to help physicians suggest medical treatments. The accuracy of the model will be evaluated to determine whether the performance of the model is accurate or not. Seven machine learning methods are compared in this study, with the data obtained from the UCI Laboratory's cardiovascular patient database. In essence, this research presents a majority voting-based hybrid model which is called triple voting. The hybrid model uses voting of Naïve Bayes, logistic regression (LR) and support vector machines (SVM) experimental outcomes show the proposed triple voting model's accuracy is 89%, which is higher than the individual models and other proposed hybrid models.

Keywords: cardiovascular disease; machine learning; majority voting; ensemble learning.

DOI: 10.1504/IJADS.2025.145881

International Journal of Applied Decision Sciences, 2025 Vol.18 No.3, pp.257 - 281

Received: 23 May 2023
Accepted: 06 Aug 2023
Published online: 30 Apr 2025 *

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