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@article{186727,
        author = {Dr.Ganesh Gorakhnath Taware and Ms.Shinde Dhanashri Dnyandeo},
        title = {Review on Cardiovascular Disease Prediction System Using Machine Learning Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2509-2513},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186727},
        abstract = {Cardiovascular diseases (CVD) are a significant global health concern, necessitating effective tools for early diagnosis and risk prediction. This study introduces a web-based Cardiovascular Disease Prediction System that applies machine learning to analyze clinical and demographic factors, including age, weight, blood pressure, cholesterol, and glucose levels. The system is built using a dataset of 70,000 patient records obtained from Kaggle, which underwent preprocessing and feature selection using ANOVA to enhance data quality and model efficiency. Several algorithms were implemented, including Random Forest, Naive Bayes, K-Nearest Neighbors, Decision Tree, and Support Vector Machine. Among these, Random Forest achieved the highest accuracy of 74%, validated through performance metrics like precision, recall, and F1-score. The system features an intuitive interface supporting secure login, user registration, data input, and automated PDF report generation. By leveraging machine learning, the system serves as a reliable tool for early CVD detection, contributing to preventive healthcare and timely interventions.},
        keywords = {Cardiovascular disease prediction, machine learning algorithms, feature selection (ANOVA), hyperparameter tuning, clinical decision support system, health risk assessment, Web Application.},
        month = {November},
        }