A Comprehensive Survey of Machine Learning and Deep Learning Methods for Cardiovascular Disease Classification
DOI:
https://doi.org/10.32628/CSEIT2511620Keywords:
Cardiovascular Diseases, Artificial Neural Network, Classification, Deep Learning, Data Mining, Machine LearningAbstract
As one of the main causes of disability and death worldwide, cardiovascular diseases (CVDs) need precise and efficient classification methods to support early diagnosis and effective clinical decision-determination. A detailed review of the categorization is given in this article of traditional statistical methods, rule -based systems, and data based modelling techniques.It highlights the types of clinical and non-clinical data, including physical signals, patient health records and medical imaging, as well as the exhibit metrics generated to evaluate classification models. The report discusses the primary issues, including feature selection, data imbalance, interpretation, and deployment constraints.In addition, it identifies novel trends and areas for further study to improving accuracy, strength and scalability in the classification of cardiovascular disease. This review serves as a means of understanding the current development for researchers and healthcare professionals and identifying the opportunities for the classification of cardiovascular disease research and advancing applications.
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