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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62046
Volume:
10 |
4 |
April 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Computer Engineering
KCE’s College
Department of Computer Engineering
KCE’s College
Department of Computer Engineering
KCE’s College
Department of Computer Engineering
KCE’s College
Heart attack alternatively known as Myocardial Infarction is one of the primary reasons of morbidity on the planet. Therefore, the diagnosis and prediction of heart disease is persuading many researchers to develop intelligent medical decision support systems. Machine Learning has been demonstrated to be viable in helping with decision making and predictions from the huge amount of clinical data delivered by the medical care. This paper aims to improve the accuracy of machine learning models which can help to make informed decision and prediction of heart attack. We have applied six machine learning classification algorithms: Support Vector Machine, Random Forest, K Nearest Neighbors, Gaussian Naive Bayes, Decision Tree and Logistic Regression. Additionally, an extensive comparison of machine learning techniques has been carried out. Our research work suggests that machine learning methods with data balancing techniques are effective tools for stroke prediction with imbalanced data. Therefore, Synthetic Minority Over-Sampling Technique (SMOTE) has been applied in our model. Hence, it is anticipated that Random Forest excels with the highest accuracy of 96% in heart attack prediction regarding performance metrics.
KEYWORDS
In conclusion, the proposed integrated healthcare system resolves the key issues of the fragmented healthcare services by creating a central and unified digital platform. The integration of hospitals, pharmacies, diagnostic centers, and patients in one digital platform will increase coordination, reduce waiting times, and improve data management, resulting in timely access to healthcare services. The inclusion of features like digital patient records, e-prescriptions, online appointment systems, telemedicine, and cloud storage will greatly increase the efficiency, transparency, and patient satisfaction of the healthcare services. Additionally, the proposed system will improve decision-making by organizing the data and facilitating real-time communication between the healthcare stakeholders. Therefore, this smart healthcare system will greatly contribute to the quality of services, efficiency, and accessibility of the healthcare services, making it a reliable and efficient way to manage modern healthcare services..
1. Google Developers. Firebase https://firebase.google.com/docs. 2. Google Maps Platform. Maps SDK for Android Documentation. Available at: https://developers.google.com/maps/documentation 3. TensorFlow Lite. Mobile Deployment Guide for AI Models. Available at: https://www.tensorflow.org/lite. 4. World Health Organization (WHO). “Digital Health and Telemedicine Guidelines.” WHO Publications, 2022. 5. Ministry of Health Family Welfare (India). “Ayushman Bharat Digital Mission (ABDM)– Health Records Framework.” Government of India, 2021.
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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