Feature Correlation and Importance Analysis for Heart Attack Prediction
Authors
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Paras Negi
Research Scholar, Soban Singh Jeena University, Almora Campus, Almora, Uttarakhand - 263 601
https://orcid.org/0000-0001-8533-5966
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Manoj Kumar Bisht
Assistant Professor, Soban Singh Jeena University, Almora Campus, Almora, Uttarakhand - 263 601
https://orcid.org/0009-0005-9595-3836
DOI:
https://doi.org/10.17010/ijcs/2025/v10/i4/175750Keywords:
Feature Importance, Heart Attack Dataset, Heart Attack Prediction, Machine Learning, Statistical Analysis.Publication Chronology: Paper Submission Date : July 2, 2025 ; Paper sent back for Revision : July 10, 2025 ; Paper Acceptance Date : July 14, 2025 ; Paper Published Online : August 5, 2025
Abstract
Cardiovascular diseases are a leading cause of global mortality, with heart attacks being a major contributor. Early detection and accurate risk evaluation are essential for reducing adverse outcomes. This study analysed a publicly available heart attack dataset to identify significant predictors of heart attack. We examined relationships between the target variable (heart attack/no heart attack) and clinical features. The findings offered insights for developing accurate and interpretable predictive models, supporting early diagnosis and improved clinical decision-making.
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References
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