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Data mining techniques for disease risk prediction model: A systematic literature review

Decision making; Decision trees; Forecasting; Health care; Soft computing; Accuracy evaluation; Classification technique; Data mining algorithm; Descriptive analysis; Disease risks; Infectious disease; Risk prediction models; Systematic literature review; Data mining

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Main Authors: Ahmad W.M.T.W., Ghani N.L.A., Drus S.M.
Other Authors: 55163807800
Format: Conference Paper
Published: Springer Verlag 2023
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spelling my.uniten.dspace-250502023-05-29T15:31:02Z Data mining techniques for disease risk prediction model: A systematic literature review Ahmad W.M.T.W. Ghani N.L.A. Drus S.M. 55163807800 56940219600 56330463900 Decision making; Decision trees; Forecasting; Health care; Soft computing; Accuracy evaluation; Classification technique; Data mining algorithm; Descriptive analysis; Disease risks; Infectious disease; Risk prediction models; Systematic literature review; Data mining Risk prediction model estimates event occurrence based on related data. Conventional statistical metrics that utilized primary data generates simple descriptive analysis that often provide insufficient knowledge for decision making. In contrast, data mining techniques that have the capability to find hidden pattern from the secondary data in large databases and create prediction for de- sired output has become a popular approach to develop any risk prediction model. In healthcare particularly, data mining techniques can be applied in disease risk prediction model to provide reliable prediction on the possibility of acquiring the disease based on individual�s clinical and non-clinical data. Due to the increased use of data mining in healthcare, this study aims at identifying the data mining techniques and algorithms that are commonly implemented in studies related to various disease risk prediction model as well as finding the accuracy of the algorithms. The accuracy evaluation consists of various method, but this paper is focusing on overall accuracy which is measured by the total number of correctly predicted output over the total number of prediction. A systematic literature review approach that search across five databases found 170 articles, of which 7 articles were selected in the final process. This review found that most prediction model used classification technique, with a focus on decision tree, neural network, support vector machines, and Na�ve Bayes algorithms where heart-related disease is commonly studied. Further research can apply similar algorithms to develop risk prediction model for other types of diseases, such as infectious disease prediction. � Springer Nature Switzerland AG 2019. Final 2023-05-29T07:31:02Z 2023-05-29T07:31:02Z 2019 Conference Paper 10.1007/978-3-319-99007-1_4 2-s2.0-85053939641 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053939641&doi=10.1007%2f978-3-319-99007-1_4&partnerID=40&md5=35f4c2c49c0a4e8ad00bc37c7501f062 https://irepository.uniten.edu.my/handle/123456789/25050 843 40 46 Springer Verlag Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Decision making; Decision trees; Forecasting; Health care; Soft computing; Accuracy evaluation; Classification technique; Data mining algorithm; Descriptive analysis; Disease risks; Infectious disease; Risk prediction models; Systematic literature review; Data mining
author2 55163807800
author_facet 55163807800
Ahmad W.M.T.W.
Ghani N.L.A.
Drus S.M.
format Conference Paper
author Ahmad W.M.T.W.
Ghani N.L.A.
Drus S.M.
spellingShingle Ahmad W.M.T.W.
Ghani N.L.A.
Drus S.M.
Data mining techniques for disease risk prediction model: A systematic literature review
author_sort Ahmad W.M.T.W.
title Data mining techniques for disease risk prediction model: A systematic literature review
title_short Data mining techniques for disease risk prediction model: A systematic literature review
title_full Data mining techniques for disease risk prediction model: A systematic literature review
title_fullStr Data mining techniques for disease risk prediction model: A systematic literature review
title_full_unstemmed Data mining techniques for disease risk prediction model: A systematic literature review
title_sort data mining techniques for disease risk prediction model: a systematic literature review
publisher Springer Verlag
publishDate 2023
_version_ 1806427809382400000
score 13.214268

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