Why the study?
Heart attack is a leading cause of death globally, but conventional methods for diagnosing heart disease are not reliable.
Does an Optimized Deep Belief Network with SVM classification accurately predict heart disease using UCI and Stalog databases?
Does an Optimized Deep Belief Network with SVM classification accurately predict heart disease using UCI and Stalog databases?
An Optimized Deep Belief Network with SVM classification achieved 97.91% accuracy in predicting heart disease using standard databases.
ML-based sensor analysis may aid timely heart failure diagnosis; leaves open need for clinical validation.
A heart attack is a common cause of death globally. It can be treated successfully through a simple and accurate diagnosis. Getting the right diagnosis at the right time is very important for the treatment of heart failure. Currently, the conventional method of diagnosing heart disease is not reliable. Machine learning is a type of artificial intelligence that can be used to analyze the data collected by sensors. Data mining is another type of technology that can be utilized in the healthcare industry. These techniques help predict heart disease based on various factors. We developed a prediction and recommendation model aimed at predicting heart disease using the Optimized Deep Belief Network. It does so by taking into account the various features of the heart disease UCI and Stalog database. Finally, the proposed method classifies healthy people and people with heart illness with an accuracy of 97.91%.
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Maheswari et al. (2022) studied this question.
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