An Extensive Review of Machine Learning and Deep Learning Techniques on Heart Disease Classification and Prediction

Pooja Rani1, Rajneesh Kumar2, Anurag Jain3, Rohit Lamba4, Ravi Kumar Sachdeva5, Karan Kumar4, Manoj Kumar6,7
1MMICTBM, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, India
2Department of Computer Engineering, MMEC, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, India
3School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, India
4Electronics and Communication Engineering Department, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, India
5Department of Computer Science & Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
6School of Computer Science, FEIS, University of Wollongong in Dubai, Dubai, UAE
7MEU Research Unit, Middle East University, Amman, Jordan

Tóm tắt

Heart disease is a widespread global concern, underscoring the critical importance of early detection to minimize mortality. Although coronary angiography is the most precise diagnostic method, its discomfort and cost often deter patients, particularly in the disease's initial stages. Hence, there is a pressing need for a non-invasive and dependable diagnostic approach. In the contemporary era, machine learning has pervaded various aspects of human life, playing a significant role in revolutionizing the healthcare industry. Decision support systems based on machine learning, leveraging a patient's clinical parameters, offer a promising avenue for diagnosing heart disease. Early detection remains pivotal in mitigating the severity of heart disease. The healthcare sector generates vast amounts of patient and disease-related data daily. Unfortunately, practitioners frequently underutilize this valuable resource. To tap into the potential of this data for more precise heart disease diagnoses, a range of machine learning algorithms is available. Given the extensive research on automated heart disease detection systems, there is a need to synthesize this knowledge. This paper aims to provide a comprehensive overview of recent research on heart disease diagnosis by reviewing articles published by reputable sources between 2014 and 2022. It identifies challenges faced by researchers and proposes potential solutions. Additionally, the paper suggests directions for expanding upon existing research in this critical field.

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Tài liệu tham khảo

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