review · Journal Of Big Data
Cardiovascular diseases remain a leading cause of mortality worldwide, driven by risk factors such as smoking, high blood pressure, elevated cholesterol, and obesity. Early detection is vital to lowering fatality rates, and artificial intelligence offers substantial potential to recognise risk patterns in large volumes of clinical data. A review of 82 research publications from 2012 to 2023 outlines the current landscape of machine learning and deep learning tools used to predict cardiovascular conditions. The analysis covers widely employed algorithms, including support vector machines, decision trees, random forests, and convolutional neural networks, alongside their reported performance metrics. It also reviews prominent datasets based on electrocardiogram and phonocardiogram signals. Primary challenges across the field include an absence of extensive, consistent datasets and the ongoing need to improve existing algorithmic models.
Cardiovascular conditions represent a major global health threat with rising fatality rates. Applying advanced machine learning to non-invasive cardiac signals, such as electrocardiograms and heart sounds, can assist medical professionals in detecting illnesses earlier. Understanding current computational techniques and data limitations helps guide the development of more dependable diagnostic tools for clinical decision-making.
The underlying diagnostic algorithms are aimed at supporting physicians with early detection and clinical decision-making using electrocardiogram and phonocardiogram signals. However, because this work is a secondary review highlighting ongoing hurdles, notably the absence of consistent datasets and the need for improved model performance, these applications appear to sit at an early research and benchmarking stage rather than being near-market solutions.
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Abstract Cardiovascular diseases (CVD) have been found to be prevalent in society, frequently ending in death. According to the findings of a recent survey, the mortality rate is increasing due to the prevalence of adult cigarette consumption, elevated blood pressure, high cholesterol levels, and obesity. The previously mentioned causes are exacerbating the severity of the condition. A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). This involves the use of advanced tools to detect the disease early on and aid in the reduction of fatality rates. With their extensive methodologies that would help in the early CVD prediction and recognition of behavioral patterns in large amounts of data, artificial intelligence, and data mining disciplines offer a broad study potential. The results of these predictions will help physicians make decisions and early diagnoses, decreasing the risk of patient death. This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. The review encompasses commonly employed methodologies such as support vector machine, decision tree, random forest, and convolutional neural networks (CNNs). Additionally, this survey aggregates and presents information on the performance metrics used to report accuracy. It also goes over the most popular datasets used by various diagnostic models (ECG and PCG signals datasets). In addition, it emphasizes prominent publishers, journals, and conferences that serve as platforms for the evaluation of scholarly works. Additionally, it will facilitate their understanding of the unresolved challenges or hurdles experienced by past researchers. A lack of more extensive and consistent datasets was the most common issue, followed by the need to improve existing models.
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DOI: 10.1186/s40537-024-01011-7
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