April 29, 2024International Journal of Innovative Science and Research Technology (IJISRT)Open Access

Predict the Heart Attack Possibilities Using Machine Learning

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Authors

PBPratik Bodake ASAkash Shevkar JPJaydeep Padwal

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Implication

Historical review demonstrates machine learning models predict heart attack risk from clinical data, highlighting pathways for real-time cardiovascular monitoring.

Key Points

Predictive models using machine learning assess heart attack likelihood effectively, identifying critical cardiac risk patterns from complex patient diagnostic records.
Historical review of clinical records evaluates demographic factors alongside electrocardiograms and blood pressure readings through structured feature engineering.
Proactive cardiovascular health management requires standardized benchmarks, and multimodal data integration may enable future real-time risk assessment systems.

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Bodake et al. (2024) studied this question.

synapsesocial.com/papers/68e6d073b6db64358764eb34 https://doi.org/10.38124/ijisrt/ijisrt24apr1878

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Implication

Historical review demonstrates machine learning models predict heart attack risk from clinical data, highlighting pathways for real-time cardiovascular monitoring.

Predict the Heart Attack Possibilities Using Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Predict the Heart Attack Possibilities Using Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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