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@article{176460,
        author = {Sahil Yadav and Varun Sakharkar and Arham Sancheti and Sagar Rghatvan},
        title = {Heart Disease Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {8052-8057},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176460},
        abstract = {This project takes advantage of machine learning techniques to predict the possibility of heart disease in individuals based on various health characteristics, from the title "predicting heart disease". A major cause of mortality due to heart diseases is, the purpose of this initiative is to increase early identity and preventive care through data-operated insights. The platform processes a dataset with significant health parameters such as age, penis, cholesterol levels, blood pressure, and exercise habits, used to train a logistic region model. This model classifies individuals into two groups: who are at risk of heart disease and which are not. The prediction system is designed to be comfortable, enabling users to input their health information and receive an accurate diagnosis. By incorporating the use of python, the panda library for data manipulation, and scikit-learn for model implementation, provides a broad, reliable solution for project heart disease prediction. In addition, the model is adapted to the user access, offering a simple interface and rapid response time. It discusses the design, implementation and testing stages of the paper system, highlighting the model's accuracy, performance matrix and potential applications in real -world healthcare scenarios. Through this platform, we aim to contribute to the growing field of future health technologies and support early intervention in cardiovascular care.},
        keywords = {Data Analysis, Heart Disease, Logistic Regression, Machine Learning, Predictive Health, Python, Scikit-learn, Accuracy Metrics, Early Detection, Healthcare, Model Training, Predictive Model, Data Preprocessing, User Interface, User-Friendly Design.},
        month = {May},
        }