research-article

A Machine learning-based prediction model for the heart diseases from chance factors through two-variable decision tree classifier

Authors: Y. Wang, Y.M. Chu, Y.A. Khan, Z.Y. Khan, Q. Liu, M.Y. Malik, S.Z. AbbasAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 41, Issue 6

Pages 5985 - 6002

https://doi.org/10.3233/JIFS-202226

Published: 01 January 2021 Publication History

Abstract

This paper addressed the prediction of heart sicknesses from hazard elements through a decision-making tree. We introduced the facts mining technique in public fitness to extract high-degree knowledge from raw data, which facilitates predicting heart diseases from risk factors and their prevention. The existing work intends to introduce a new risk element in heart diseases using novel data mining strategies. Latest actual international affected person’s information (e.g., smoking, area of residence, age, weight, blood stress, chest pain, low-density lipoproteins (LDL), high-density lipoproteins (HDL), block arteries became accrued by way of the use of questionnaire through direct interview technique from patients. Novel two-variable decision trees are constructed for coronary heart illness records primarily based on chance factors and ranking of risk elements. The results show a correct prediction of cardiovascular disease (CVD) from the risk factor if records on chance factors are available as direct results of this study, tobacco, loss of physical exercise, and weight-reduction plan play a vital role in predicting heart diseases, which is the most important reason for mortality in developing countries, especially in my country.

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Cited By

Zeng WYuan C(2023)Epileptic EEG Identification Based on Dual Q-Factor Signal Decomposition (DQSD), Fast and Adaptive Multivariate Empirical Mode Decomposition (FA-MVEMD) and Neural NetworksCircuits, Systems, and Signal Processing10.1007/s00034-022-02282-242:6(3552-3588)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s00034-022-02282-2

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A Machine learning-based prediction model for the heart diseases from chance factors through two-variable decision tree classifier

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cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 41, Issue 6

2021

1780 pages

ISSN:1064-1246

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© 2021 – IOS Press. All rights reserved.

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IOS Press

Netherlands

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Published: 01 January 2021

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Cited By

Zeng WYuan C(2023)Epileptic EEG Identification Based on Dual Q-Factor Signal Decomposition (DQSD), Fast and Adaptive Multivariate Empirical Mode Decomposition (FA-MVEMD) and Neural NetworksCircuits, Systems, and Signal Processing10.1007/s00034-022-02282-242:6(3552-3588)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s00034-022-02282-2

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