Enhancing Heart Disorders Prediction with Attribute Optimization

Mishra, Sushruta; Dash, Anuttam; Ranjan, Piyush; Jena, Ajay Kumar

doi:10.1007/978-981-15-8752-8_14

Cited by 18 publications

(1 citation statement)

References 3 publications

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“…A large amount of patient"s vitals can also be obtained from wearable devices which help in data collection and analysis [40]. These vital signs can be combined with electronic medical records (EMR) to improve the feature set [41].UCI data repositories are also another source to provide benchmark heart disease datasets [42,43].…”

Section: Resultsmentioning

confidence: 99%

A Knowledge-Based Clinical Decision Support System Utilizing an Intelligent Ensemble Voting Scheme for Improved Cardiovascular Disease Prediction

Bashir¹,

Almazroi

Ashfaq

et al. 2021

IEEE Access

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A massive amount of medical data is available in healthcare industry, which can be utilized to extract useful knowledge. A Clinical Decision Support System (CDSS) is used to improve patient"s safety by minimizing medical errors. Heart disease is one of the major chronic maladies even in todays" world. Many researchers have employed different data mining techniques to predict heart disease. The objective of proposed framework is to improve the accuracy of heart disease prediction. In this paper, an ensemble based voting scheme is proposed to efficiently predict heart disease. Four benchmark heart disease datasets from UCI repository have been utilized for experimentation and evaluation. The performance of the proposed ensemble is compared with individual classifiers as well as with five different ensemble schemes using various parameters in order to show the effectiveness of the proposed ensemble scheme. The evaluation of results shows that the proposed ensemble scheme has better average accuracy (83%) as compared to other ensemble schemes as well as individual classifiers.

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