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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67903
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
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.
computer science and engineering
BIET LUCKNOW
computer science and engineering
BIET LUCKNOW
Our main objective is to develop a Cardiovascular Disease (CVD) Risk Prediction model Using Supervised Learning Classifiers that can be used in making expert decision with maximum accuracy of having a heart disease or not. It will prove very important for medical fields for diagnosing diseases related to heart such as heart attack, heart failure, strokes and other coronary artery diseases. If such predictions make good results with enough accuracy, we cannot avoid inaccurate diagnosis. There are also save unnecessary resources used from the real word. When a patient who is not having a hearts disease is diagnosed positive, he takes unnecessary panic and when patients who have actually a hearts disease and there are not diagnosed with hearts disease or we cannot get negative results, there will unwillingly miss that chance to cure his/her disease. Such kind of method is follow wrong diagnosis which is disadvantageous to both patients as well as hospitals. Therefore for better accuracy of predictions, we can overcome the unnecessary troubles. So, for this we can provide our best model as Random forest Classifier which shows more accuracy of 91.59% for detecting heart disease in a person among all our three classifiers whatever we used in our project. The objective of our project is to save human life in medical centers and hospital that improve the accuracy of disease diagnosis.
KEYWORDS
The proposed system can help the healthcare professional to take faster and more accurate decisions for early diagnosis of heart disease.In general, the experimental results indicate that the Random Forest classifier is a robust andreliable solution for predicting cardiovascular diseases and can contribute significantly to intelligent healthcare systems and clinical decision support applications.
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Publication Date
10/14/2026
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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