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@article{173203,
        author = {Jaiprakash Singh Yadav and Tripty Yadav},
        title = {Machine Learning Approaches for Predicting Heart Diseases},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2574-2582},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173203},
        abstract = {heart disease remains one of the leading causes of mortality worldwide, making early detection crucial for effective treatment and prevention. Traditional diagnostic methods often face limitations in terms of time, cost, and accuracy, prompting the exploration of machine learning (ML) algorithms for heart disease prediction. This paper investigates various ML techniques, focusing on supervised learning models like Decision Trees, Support Vector Machines (SVM), and neural networks. These methods aim to improve the accuracy and efficiency of diagnosis by analyzing heart disease datasets. The paper also addresses key challenges such as data quality, feature selection, and model evaluation. Data quality can affect the reliability of predictions, while feature selection plays a crucial role in identifying the most relevant factors for accurate diagnosis. Furthermore, evaluating model performance is essential for determining the most effective approach to predicting heart disease. Overall, the findings suggest that machine learning offers significant potential in enhancing diagnostic accuracy for heart disease. By leveraging these techniques, healthcare professionals can make more informed decisions and implement early intervention strategies, ultimately improving patient outcomes.},
        keywords = {Heart Disease Prediction, Decision Tree, Support Vector Machine, Clustering, Random Forest, Machine Learning Algorithms, Focusing, Predictive Models},
        month = {February},
        }