Heart Disease Prediction Using Fuzzy System

Sharma, Sumit; Madaan, Vishu; Agrawal, Prateek; Garg, N. K.

doi:10.1007/978-981-13-3140-4_38

Cited by 4 publications

(1 citation statement)

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“…The expert system was developed in MATLAB and the proposed framework had 84.7% accuracy in the prediction of heart disease. The same approach was used in Sharma, Madaan [83] to develop an expert system for heart disease prediction using up to 19 risk features along with ECG recording. The system was able to predict low, middle and high risk of heart disease in patients.…”

Section: Fuzzy-based Methodsmentioning

confidence: 99%

Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

et al. 2022

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Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.

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Section: Fuzzy-based Methodsmentioning

confidence: 99%