HEART FAILURE PREDICTION SYSTEM USING MACHINE LEARNING

Heart failure is a critical medical condition that requires early detection to improve patient outcomes.This paper presents a machine learning-based heart failure prediction system that utilizes various classification algorithms to analyze patient data and predict the likelihood of heart failure.The study compares models such as Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Networks.The results indicate that machine learning models significantly enhance predictive accuracy, aiding in timely medical interventions.

Authors

Publication Details

Journal
International Research Journal of Education and Technology (IRJET).
Published
2024-11-29
DOI
https://doi.org/10.70127/irjedt.vol.7.issue02.302
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

HEART FAILURE PREDICTION SYSTEM USING MACHINE LEARNING

S Sooryaprakash
International Research Journal of Education and Technology (IRJET).
Artificial Intelligence in Healthcare
article

HEART FAILURE PREDICTION SYSTEM USING MACHINE LEARNING

S Sooryaprakash
article en

Abstract

Heart failure is a critical medical condition that requires early detection to improve patient outcomes.This paper presents a machine learning-based heart failure prediction system that utilizes various classification algorithms to analyze patient data and predict the likelihood of heart failure.The study compares models such as Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Networks.The results indicate that machine learning models significantly enhance predictive accuracy, aiding in timely medical interventions.

International Research Journal of Education and Technology (IRJET).Vol. 6(11)
Openalex Percentile: Top 62%
Artificial Intelligence in Healthcare
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HEART FAILURE PREDICTION SYSTEM USING MACHINE LEARNING — S Sooryaprakash · International Research Journal of Education and Technology (IRJET). (2024) | TGRS Research Map | TGRS