Key result
Deep learning models undergo systematic review to evaluate diagnostic effectiveness for heart disease detection.
Why the study?
Significant advancements in deep learning have been applied to heart disease prediction, but a comprehensive review of these methods from 2017 to 2022 was needed.
How effective are various deep learning models in identifying cardiac disease?
Systematic Review
How effective are various deep learning models in identifying cardiac disease?
This systematic review evaluates the effectiveness of various deep learning models for the diagnosis and prediction of heart disease based on literature from 2017 to 2022.
May guide model selection for cardiac detection; leaves open need for prospective validation before practice change.
Heart disease ranks among the primary factors contributing to mortality worldwide. They can have hypertensive, and vascular heart disease. The significant advancements in medical technologies that employ data mining and deep learning (DL) have become crucial in predicting cardiovascular diseases. Researchers and practitioners have suggested various solutions at both the data level and algorithm level for many years. Therefore, the research works on heart disease detection based on deep learning are reviewed in this paper. The review paper focused on the papers in the period of 2017 to 2022. Additionally, a comparison is used to assess the effectiveness of several deep-learning models in identifying cardiac disease.
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Vidya et al. (2023) conducted a systematic review in Heart disease. Deep learning models vs. Various deep-learning models was evaluated on Effectiveness in identifying cardiac disease. Deep learning models for heart disease detection were systematically reviewed and compared to assess their diagnostic effectiveness.
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