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@article{182145,
        author = {Shravani Suresh Avalkar and Dr. Santosh Shivraj Chowhan},
        title = {AI – Driven Heart Health: Enabling Early and Accurate Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1123-1148},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182145},
        abstract = {An accurate diagnosis of heart disease is important for timely intervention and proper treatment. This paper reviews heart disease prediction through machine learning approaches using the Cleveland Heart Disease dataset. We analysed the performance of several classification algorithms using SVM, KNN, Logistic Regression, Decision Tree, Random Forest, and XGBoost. The pre-evaluation metrics were accuracy, precision, recall, and F1-score in order to determine the best applicable model for heart disease detection. SVM was found to have the highest classification accuracy of 90.16%. KNN also had high accuracy at 91.80%, performing well in classifying both uninhibited and affected users. Logistic Regression had poorer accuracy at 88.52%, but was still a split choice due to simplicity and practicality. The accuracy of Random Forest and XGBoost was 86.89% and 85.25%, respectively, demonstrating continually accurate predictions. The Decision Tree, however, had the lowest accuracy of all at 72.13%. This shows that underfitting and overgeneralizing were a problem for the model. This research demonstrates the necessity of clinical relevance and tailoring model selection to the dataset. The use of KNN and SVM algorithms makes it possible to improve the accuracy of diagnosis and patient treatment by reducing the rate of errors. Moreover, the work shows the contribution of machine learning to the heart disease predictive analytics improvement. Utilizing advanced classification models enables these practitioners to make decisions based on factual information, resulting in better care of cardiovascular conditions. This is important among medical doctors and researchers working on ways to enhance the use of diagnostic systems and further illustrate the consequences of advanced technological methods like machine learning on healthcare analytics.},
        keywords = {Classification models, Diagnostics of Cardiovascular Diseases, Heart Disease Prediction, KNN, Machine Learning, Predictive Analytics, SVM.},
        month = {July},
        }