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@article{198405,
        author = {Bharanidharan P and Gowtham S and Devanesh R and Ramkumar D},
        title = {Ai based heart disease prediction using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {16233-16239},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198405},
        abstract = {heart disease remains one of the leading causes of mortality globally, influenced by factors such as stress, genetic predisposition, high blood pressure, and lifestyle habits. Early and accurate prediction of cardiovascular conditions is critical to reduce associated risks and improve patient outcomes. Traditional diagnostic methods and statistical models often struggle with the complexity and volume of modern medical datasets, leading to delayed or inaccurate assessments. This project proposes a scalable cloud-based heart disease prediction system leveraging the Random Forest machine learning algorithm to analyze multiple patient health parameters, including age, blood pressure, cholesterol, heart rate, and other relevant clinical data. By employing ensemble learning techniques, the system enhances predictive accuracy, reduces overfitting, and identifies key risk factors, providing reliable and interpretable results. Cloud integration ensures efficient handling of large datasets and supports real-time decision-making for healthcare professionals. The proposed system not only improves diagnostic reliability but also promotes preventive care and personalized intervention strategies, contributing to better management of cardiovascular diseases and reduced mortality rates worldwide.},
        keywords = {Heart Disease, Cardiovascular Risk, Random Forest, Machine Learning, Predictive Analytics, Cloud-Based System, Ensemble Learning, Early Diagnosis, Patient Health Monitoring, Preventive Healthcare},
        month = {April},
        }