Machine Learning Approaches to Heart Attack Risk Detection and Classification

Registration ID: IJNRD_214719

Published ID: IJNRD2403074

DOI: Click Here to Get

Talasila Dheeraj , Skanda P R

Cardiovascular diseases, Classification model, Deep learning, Heart attack prediction, Machine learning

Cardiovascular diseases, including heart attacks, remain one of the leading causes of mortality worldwide. Early detection and accurate classification of individuals at risk of experiencing a heart attack are crucial for taking preventive measures. In this research paper, we explore various machine-learning algorithms for heart attack prediction and classification. Leveraging a dataset comprising diverse parameters, we utilize various machine-learning techniques. Our study aims to develop an efficient predictive model capable of identifying individuals susceptible to heart attacks and effectively classifying them. Through comprehensive experimentation and evaluation, we assess the performance of these models, thereby contributing to the advancement of cardiovascular health management.

Published Paper   E-Certificate

Talasila Dheeraj & Skanda P R (March-2024). Machine Learning Approaches to Heart Attack Risk Detection and Classification . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), a679-a683. https://ijnrd.org/papers/IJNRD2403074.pdf

Volume 9 Issue 3, March-2024

Pages : a679-a683

Paper Reg. ID: IJNRD_214719

Published Paper Id: IJNRD2403074

Downloads: 000121980

Research Area: Computer Science & Technology 

Country: BANGALORE, KARNATAKA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403074.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403074

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 10 | October 2025

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