Data pre-processing for cardiovascular disease classification: A systematic literature review

Javid, Irfan and Ghazali, Rozaida and Muhammad Zulqarnain, Muhammad Zulqarnain and Hassan, Norlida (2023) Data pre-processing for cardiovascular disease classification: A systematic literature review. Journal of Intelligent & Fuzzy Systems, 44. pp. 1525-1545.

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Abstract

The important task in the medical field is the early detection of disease. Heart disease is one of the greatest challenging diseases in all other diseases subsequently 17.3 million people died once a year due to heart disease. A minute error in heart disease diagnosis is a risk for an individual lifespan. Precise heart disease diagnosis is consequently critical. Different approaches including data mining have been used for the prediction of heart disease. However, there are some solemn concerns related to the data quality for example inconsistencies, missing values, noise, high dimensionality, and imbalanced statistics. In order to improve the accuracy of Data Mining based prediction systems, techniques for data preparation were applied to increase the quality of the data. The foremost objective of this paper is to highlight and summarize the research work about (i) data preparation techniques mostly used, (ii) the impact of pre-processing procedures on the accuracy of a heart disease prediction system, (iii) classifier enactment with data pre-processing techniques, (4) comparison in terms of accuracy of the different pre-processing model. A systematic literature review on the use of data pre-processing in heart disease diagnosis is carried out from January 2001 to July 2021 by studying the published material. Almost 30 studies were designated and examined related to the above-mentioned benchmarks. The literature review concludes that data reduction and data cleaning pre-processing techniques are mostly used in heart disease prediction systems. Overall this study concludes that data pre-processing has improved the accuracy of models used for heart disease prediction. Some hybrid models including (ANN+CHI), (ANN+PCA), (DNN+CHI) and (SVM+PCA) have shown improved accuracy level. However, due to the lack of clarification, there is a number of limitations and challenges in order to implementing these models in the real world.

Item Type:	Article
Uncontrolled Keywords:	Heart disease, data pre-processing, cardiomyopathy, data mining, literature review
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Computer Science and Information Technology > Department of Information Security
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	17 Jul 2023 07:45
Last Modified:	17 Jul 2023 07:45
URI:	http://eprints.uthm.edu.my/id/eprint/9247

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