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@article{166953,
        author = {Saloni Choudhary and Pratik Dave and Aryaman Jha and Jenil Kumbhani},
        title = {Heart Disease Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {37-40},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166953},
        abstract = {A prevalent cardiovascular condition that can alter an individual's life, heart disease is very expensive for both individuals and healthcare systems. Effective prevention strategies and early diagnosis are necessary as it progresses to be a major global societal well-being concern. Improving patient outcomes and cutting costs associated with healthcare requires early detection and proactive management. Based on a variety of patient characteristics and medical data, machine-learning characteristics, classifiers, and algorithms have demonstrated promise in the past several years in the prediction of cardiac disease risk. This abstract describes the application of machine learning methods to the construction of a prediction system for cardiac disease. This abstract analyzes patient data, such as electronic health records (EHR), medical histories, vital signs, and diagnostic test results, using sophisticated algorithms. The principal aim of the system is to precisely detect individuals who are susceptible to heart failure through the application of particle swarm optimization, forward selection, and backward elimination techniques. This will facilitate early intervention and tailored care, with 0 signifying the non-existence of heart disease and 1 signifying its presence.},
        keywords = {Machine Learning Algorithms, EHR, Particle Swarm Optimization, Forward Selection, Backward Elimination.},
        month = {July},
        }