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@article{181558,
        author = {Arthika L and Ashika J and Akhila Judit Nisha S and Jegana.R},
        title = {Heart disease prediction and recommendation using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4475-4479},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181558},
        abstract = {This heart disease prediction project uses machine learning to identify patients at risk by analyzing medical data with four algorithms: Support Vector Machine (SVM), Naive Bayes, Decision Tree, and XGBoost. The system examines factors like age, sex, chest pain type, blood pressure, cholesterol, and other key indicators to predict heart disease risk. The main goal is to classify patients into those with heart disease (label 1) and those without (label 0). The Decision Tree algorithm performed the best due to its strong accuracy, simplicity, and easy-to-understand structure. The project includes a data preprocessing stage, where missing values are handled, numerical features are scaled, and categorical variables are encoded to prepare the data for training. Feature selection techniques are used to focus on the most important data points, improving model performance and efficiency. The Decision Tree’s easy interpretability is a key advantage in healthcare. Its flowchart structure helps doctors understand how the model makes predictions, building trust and transparency. This is especially important because healthcare professionals can use the model's insights to make informed decisions. The system also provides personalized recommendations to help patients manage their heart health. These include advice on medications (under a doctor's supervision), lifestyle changes like stress management and smoking cessation, and a heart-healthy diet and exercise plan. By combining predictive analytics with health recommendations, the project aims to improve preventive care. It not only identifies people at risk early but also educates them on steps they can take to reduce their risk, ultimately improving their quality of life.},
        keywords = {},
        month = {June},
        }