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Missing data imputation with hybrid feature selection for fertility dataset

Missing values poses a great concern in medical analysis as it may alter the result of analysed data and cloud the judgement of the medical practitioner which ultimately affecting the precise treatment a patient should receive. Even though there are many imputation methods that have been developed...

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Main Authors:	Dzulkalnine, Mohamad Faiz, Sallehuddin, Roselina, Mohd. Zain, Azlan, Mohd. Radzi, Nor Haizan, Mustaffa, Noorfa Hazlinna
Format:	Article
Published:	Academy of Sciences Malaysia 2020
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/90099/ http://dx.doi.org/10.32802/asmscj.2020.sm26(5.23)
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id	my.utm.90099
record_format	eprints
spelling	my.utm.900992021-03-31T06:38:12Z http://eprints.utm.my/id/eprint/90099/ Missing data imputation with hybrid feature selection for fertility dataset Dzulkalnine, Mohamad Faiz Sallehuddin, Roselina Mohd. Zain, Azlan Mohd. Radzi, Nor Haizan Mustaffa, Noorfa Hazlinna QA75 Electronic computers. Computer science Missing values poses a great concern in medical analysis as it may alter the result of analysed data and cloud the judgement of the medical practitioner which ultimately affecting the precise treatment a patient should receive. Even though there are many imputation methods that have been developed, the main issues with missing values such as accuracy and bias in prediction remain unsolved. In this paper, Fuzzy c-means (FCM) is employed as the imputation method. However, FCM does not consider the factor of irrelevant features. Noise and redundant data in the irrelevant features can reduce the accuracy of imputation and increase the computational time of FCM. An approach to tackle this problem is by using a feature selection method. By removing features that are irrelevant, the accuracy of imputation can be increased. Therefore, in this study, a hybrid imputation model Principal Component Analysis-Support Vector Machines-FCM (PCA-SVM-FCM) is proposed. The effectiveness of the proposed model is tested on a medical dataset which is Fertility dataset. Its performance is then validated by comparing it with SVM-FCM. Experimental result demonstrated that the proposed model performs better than SVM-FCM by producing a much lower error in estimation when tested using RMSE and MAE. The proposed model was then further verified by using Thiel’s U test and producing lowU value that indicates it is sufficient and significant. Therefore, PCA-SVM-FCM can be a feasible imputation tool to assist medical practitioner to obtain a reliable and better data analysis result. Academy of Sciences Malaysia 2020-02 Article PeerReviewed Dzulkalnine, Mohamad Faiz and Sallehuddin, Roselina and Mohd. Zain, Azlan and Mohd. Radzi, Nor Haizan and Mustaffa, Noorfa Hazlinna (2020) Missing data imputation with hybrid feature selection for fertility dataset. ASM Science Journal, 13 . pp. 1-6. ISSN 1823-6782 http://dx.doi.org/10.32802/asmscj.2020.sm26(5.23) DOI:10.32802/asmscj.2020.sm26(5.23)
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Dzulkalnine, Mohamad Faiz Sallehuddin, Roselina Mohd. Zain, Azlan Mohd. Radzi, Nor Haizan Mustaffa, Noorfa Hazlinna Missing data imputation with hybrid feature selection for fertility dataset
description	Missing values poses a great concern in medical analysis as it may alter the result of analysed data and cloud the judgement of the medical practitioner which ultimately affecting the precise treatment a patient should receive. Even though there are many imputation methods that have been developed, the main issues with missing values such as accuracy and bias in prediction remain unsolved. In this paper, Fuzzy c-means (FCM) is employed as the imputation method. However, FCM does not consider the factor of irrelevant features. Noise and redundant data in the irrelevant features can reduce the accuracy of imputation and increase the computational time of FCM. An approach to tackle this problem is by using a feature selection method. By removing features that are irrelevant, the accuracy of imputation can be increased. Therefore, in this study, a hybrid imputation model Principal Component Analysis-Support Vector Machines-FCM (PCA-SVM-FCM) is proposed. The effectiveness of the proposed model is tested on a medical dataset which is Fertility dataset. Its performance is then validated by comparing it with SVM-FCM. Experimental result demonstrated that the proposed model performs better than SVM-FCM by producing a much lower error in estimation when tested using RMSE and MAE. The proposed model was then further verified by using Thiel’s U test and producing lowU value that indicates it is sufficient and significant. Therefore, PCA-SVM-FCM can be a feasible imputation tool to assist medical practitioner to obtain a reliable and better data analysis result.
format	Article
author	Dzulkalnine, Mohamad Faiz Sallehuddin, Roselina Mohd. Zain, Azlan Mohd. Radzi, Nor Haizan Mustaffa, Noorfa Hazlinna
author_facet	Dzulkalnine, Mohamad Faiz Sallehuddin, Roselina Mohd. Zain, Azlan Mohd. Radzi, Nor Haizan Mustaffa, Noorfa Hazlinna
author_sort	Dzulkalnine, Mohamad Faiz
title	Missing data imputation with hybrid feature selection for fertility dataset
title_short	Missing data imputation with hybrid feature selection for fertility dataset
title_full	Missing data imputation with hybrid feature selection for fertility dataset
title_fullStr	Missing data imputation with hybrid feature selection for fertility dataset
title_full_unstemmed	Missing data imputation with hybrid feature selection for fertility dataset
title_sort	missing data imputation with hybrid feature selection for fertility dataset
publisher	Academy of Sciences Malaysia
publishDate	2020
url	http://eprints.utm.my/id/eprint/90099/ http://dx.doi.org/10.32802/asmscj.2020.sm26(5.23)
_version_	1696976261231935488
score	13.211869

Missing data imputation with hybrid feature selection for fertility dataset

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