Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{171396,
        author = {SINCHAN S and SHASHWAT AK and SOMESHA HM and SHARAN KUMAR and NANDEESH K and Dr CHANDRAKALA  V},
        title = {A SUPERVISED LEARNING APPROACH FOR  CARDIOVASCULAR DISEASE USING  CLINICAL PARAMETERS BY MACHINE  LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3404-3409},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171396},
        abstract = {The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k- nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc.},
        keywords = {Cardiovascular disease (CVD), Artificial intelligence, Machine Learning (ML), Support Vector Machine (SVM)},
        month = {December},
        }