Abstract
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.
Highlights
Large amount of medical data is generated in medical organizations such as hospitals and clinics on daily basis; this data cannot be used intelligently until useful knowledge is extracted
According to World Health Organization (WHO) 17.7 million people died from cardiovascular diseases (CVDs) in 2015 which makes CVDs to be the cause of 31% of the total deaths
Data mining techniques have been used to design clinical decision support system (CDSS) which is in essence a model that predicts CVDs
Summary
Large amount of medical data is generated in medical organizations such as hospitals and clinics on daily basis; this data cannot be used intelligently until useful knowledge is extracted. Medical errors are responsible for deaths throughout the world, even a single medical error can lead to sudden death These medical errors can effectively be reduced by more accurate data mining techniques for disease prediction. Several data mining techniques have been introduced by researchers to improve the accuracy in medical health community. Data mining techniques have been used to design clinical decision support system (CDSS) which is in essence a model that predicts CVDs. CDSS provides necessary knowledge to diagnose/predict any disease with high accuracy [12,13]. Non-knowledge based CDSS displays results of patients‟ clinical data in a simplified manner. The proposed research focuses on knowledge-based CDSS where a heart disease diagnosis framework is proposed that results in high accuracy of heart disease prediction.
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