1. Heart diseases classification through deep learning techniques: A review.
- Author
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Ali, Shatha M., Abbosh, Younis M., Breesam, Aqeel Majeed, Ali, Dia M., and Alhummada, Iman A.
- Subjects
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HEART diseases , *BOLTZMANN machine , *NOSOLOGY , *HEART failure , *MYOCARDIAL infarction , *DEEP learning - Abstract
One of the leading causes of loss of life globally is heart disease. This refers to conditions that affect the heart's normal functioning, such as heart attacks and heart failure. Heart disease stands out as a major contributor to global mortality, and early detection performs a crucial function in enhancing the chances of recovery. In trendy years, deep learning (DL) strategies have established promising outcomes in numerous medical applications, which include several types of heart illnesses. DL algorithms can robotically extract applicable capabilities from unprocessed information, making them adequately appropriate for reading complex clinical datasets. By training on big quantities of categorized information, deep studying fashions can discover ways as needed to classify remarkable varieties of heart ailments based totally on numerous enter modalities collectively with electrocardiograms (ECG's), echocardiograms, and scientific pictures. Several studies have confirmed the effectiveness of DL in classifying heart diseases with excessive accuracy and performance. These models are constrained in their ability to effectively identify precise cardiac conditions. However also anticipate future cardiovascular occasions based on risk factors. To conclude, the use of DL to know strategies for the magnificence of heart illnesses shows splendid potential in enhancing evaluation and prognosis. Further investigation and progress within this domain may lead to more accurate and efficient approaches for early detection. This paper comprehensively surveys DL strategies for detecting heart diseases. Several important points that the researchers rely on in their work to obtain the best results were emphasized. Firstly, a comprehensive comparison was made between the research papers, then a focus was made on the techniques used in feature extraction and their impact on the work of deep learning techniques while considering that the same dataset was used. Finally, it was concluded that the highest accuracy obtained when CNN and continuous wavelet transform (CWT(algorithms were applied was 99.6%. The highest accuracy reached when using an elephant herding optimizer turned restricted Boltzmann machine network (EHO-RBM) in 2023 was 99.96%. Multiple Deep Learning methodologies were addressed in this paper. The implementation of these methodologies is categorized according to distinct metrics, and the datasets used for preparation and testing undergo thorough analysis. A complete evaluation of DL strategies for heart disease was given in this overview paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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