Key result
This review summarizes recent research on the application of machine learning and data mining techniques for the prediction and diagnosis of heart diseases.
Why the study?
Deaths from heart disease are rising and healthcare data remains largely unstructured and underutilized, requiring a comprehensive synthesis of recent research on machine learning and data mining for heart disease prediction.
Answers are grounded in this paper and cite their sources.
This review highlights the potential of machine learning and data mining techniques to utilize unstructured healthcare data for accurate and timely prediction of heart diseases.
Deaths due to heart disease are on the rise. Old people as well as relatively young people are succumbing to death because of heart failures. Chronic heart failures affect more than 26 millions people worldwide. This number is increasing 2% annually. Therefore it is needed for accurate diagnosis and at accurate time. The Healthcare industry produces lots of data. But this data is unstructured and not accessible easily. This data is not studied properly to extract information out of data. By using modern techniques like data mining and machine learning techniques we can get knowledge that can be used to diagnose heart diseases. Today a lot of research is happening on prediction of heart diseases. It is really important to study this research comprehensively. The sole target of this review paper is to study and summarize recent research carried out on heart disease predictions.
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Jadhav et al. (2023) conducted a review in Heart diseases. Machine learning and data mining techniques was evaluated. This review summarizes recent research on the application of machine learning and data mining techniques for the prediction and diagnosis of heart diseases.
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