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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM42846

Predicting Heartstroke Using Machine Learning and Generative AI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM42846

Volume:

9 |

3 |

Mar 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM42846

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RB

Rohit Bokam

CYRS

Chinta Yogitha Ratna Sri

Ajr

Allu jagadeeshwar rao

BNK

Bocha Naveen Kumar

KPK

K. Pavan Kumar

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Abstract

Heart strokes are among the leading causes of death and disability worldwide, with early detection being critical to reducing their impact. This project focuses on developing an AI-powered solution to predict heart stroke risks based on key health metrics, such as age, hypertension, heart disease history, BMI, glucose levels, and lifestyle factors like smoking status. The solution integrates a machine learning-based predictive system with generative AI to enhance user experience. Generative AI provides personalized health suggestions, precautions, and reminders for medication and regular checkups, empowering users to take proactive measures toward better health. The deployment framework includes FastAPI for the backend, PostgreSQL for secure data management, and an intuitive web application for user interaction. This project showcases the transformative potential of AI in healthcare, enabling early diagnosis and fostering preventive care strategies to reduce stroke incidence and improve patient outcomes.

KEYWORDS

Heart Stroke Prediction Machine Learning Early Diagnosis Classification Generative AI

Cite This Article

Rohit Bokam, Chinta Yogitha Ratna Sri, Allu jagadeeshwar rao, Bocha Naveen Kumar, K. Pavan Kumar, "Predicting Heartstroke Using Machine Learning and Generative AI," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 3, Mar 2025. https://doi.org/10.55041/IJSREM42846

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Publication Date

3/23/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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