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@article{166707,
        author = {Dr. Harsh mathur and Pawan Gupta},
        title = {Design and Development of an Efficient Heart Disease Prediction System Using Comprehensive Hybrid Machine Learning Algorithm: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1900-1912},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166707},
        abstract = {Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient predictive systems for early diagnosis and treatment. Traditional prediction methods often fall short in terms of accuracy and reliability. In recent years, hybrid machine learning algorithms have emerged as a powerful tool for improving the performance of heart disease prediction systems. This paper provides a comprehensive survey of the design and development of such systems, focusing on the integration of various machine learning techniques into hybrid models. We analyse the different machine learning algorithms commonly used, including logistic regression, decision trees, support vector machines, neural networks, and ensemble methods, among others. The survey explores how these algorithms can be combined to leverage their individual strengths and mitigate their weaknesses. We also examine the datasets and features typically used in heart disease prediction, highlighting the importance of feature selection and engineering in enhancing model performance. Additionally, we discuss the evaluation metrics used to assess the effectiveness of these systems. By reviewing the current state-of-the-art approaches, we identify the strengths and limitations of existing models and suggest directions for future research. This survey aims to provide researchers and practitioners with a detailed understanding of the landscape of hybrid machine learning algorithms in heart disease prediction, ultimately contributing to the development of more accurate and robust predictive systems.},
        keywords = {},
        month = {July},
        }