Authors :
Pratik Bodake; Akash Shevkar; Jaydeep Padwal; Yogeshwari Hardas
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/cyjd4ty9
Scribd :
https://tinyurl.com/2v2exrpy
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1878
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Heart disease remains one of the leading
causes of mortality worldwide, making early detection
and prevention crucial. Machine learning techniques
offer promising avenues for predicting heart attack
possibilities by analyzing patient data and identifying
risk factors. This study explores the development of a
predictive model using machine learning algorithms to
assess the likelihood of a heart attack based on
individual patient characteristics and medical history.
The dataset comprises a comprehensive range of
features including demographic information, lifestyle
factors, medical history, and results from diagnostic tests
such as electrocardiograms (ECG), cholesterol levels,
and blood pressure readings. Preprocessing techniques
such as data cleaning, normalization, and feature
engineering are applied to prepare the dataset for
analysis. Looking ahead, the article identifies promising
avenues for future research, including the integration of
multimodal data sources, real-time risk assessment
systems, and collaborative efforts to develop
standardized benchmarks and evaluation protocols. By
synthesizing the collective knowledge gleaned from
decades of research, this historical review aims to inform
and inspire ongoing endeavors in leveraging machine
learning for proactive cardiovascular health
management and prevention strategies.
Keywords :
Support Vector Machine ,Machine Learning Algorithm, Computational Modeling.
Heart disease remains one of the leading
causes of mortality worldwide, making early detection
and prevention crucial. Machine learning techniques
offer promising avenues for predicting heart attack
possibilities by analyzing patient data and identifying
risk factors. This study explores the development of a
predictive model using machine learning algorithms to
assess the likelihood of a heart attack based on
individual patient characteristics and medical history.
The dataset comprises a comprehensive range of
features including demographic information, lifestyle
factors, medical history, and results from diagnostic tests
such as electrocardiograms (ECG), cholesterol levels,
and blood pressure readings. Preprocessing techniques
such as data cleaning, normalization, and feature
engineering are applied to prepare the dataset for
analysis. Looking ahead, the article identifies promising
avenues for future research, including the integration of
multimodal data sources, real-time risk assessment
systems, and collaborative efforts to develop
standardized benchmarks and evaluation protocols. By
synthesizing the collective knowledge gleaned from
decades of research, this historical review aims to inform
and inspire ongoing endeavors in leveraging machine
learning for proactive cardiovascular health
management and prevention strategies.
Keywords :
Support Vector Machine ,Machine Learning Algorithm, Computational Modeling.
