Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko

Breast cancer is a deadly disease caused by the uncontrolled growth of cells that starts in the breast. Therefore, the accurate risk prediction is crucial in assisting the selection for the suitable prevention treatment, depending on the level of the risk. However, the abundance of biomedical data f...

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Main Author:	Jusoh, Fatimatufaridah
Format:	Thesis
Language:	English
Published:	2014
Subjects:	RC0254 Neoplasms Tumors Oncology (including Cancer)
Online Access:	http://eprints.utm.my/id/eprint/48058/25/FatimatufaridahJusohMFK2014.pdf
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id	my-utm-ep.48058
record_format	uketd_dc
spelling	my-utm-ep.480582017-08-16T05:45:11Z Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko 2014-03 Jusoh, Fatimatufaridah RC0254 Neoplasms. Tumors. Oncology (including Cancer) Breast cancer is a deadly disease caused by the uncontrolled growth of cells that starts in the breast. Therefore, the accurate risk prediction is crucial in assisting the selection for the suitable prevention treatment, depending on the level of the risk. However, the abundance of biomedical data from various sources creates difficulty in data organizing. In addition, the big challenge in predicting the risk of breast cancer is the different attributes of the datasets which make it inscrutable for someone who are not from the domain background. Ontology is a new method introduced to improve the knowledge discovery in complex database. Ontology approach was applied in this study to resolve this problem by providing clearer understanding of the data. In this study, ontology was also used to select important features for data analysis. Classification technique of Sequential Minimal Optimization (SMO) was also applied in this study. SMO is a fast learning algorithm of Support Vector Machine (SVM) and able to provide high accuracy results. However, the analysis of breast cancer risk shows that data analysis without ontology has slightly higher accuracy compared to data analysis with ontology, where, the first dataset is 94.7% compared to 92.1% and the accuracy for the second dataset is 96.7% compared to 96.6%. These results were different from expectation, which the application of ontology was supposed to be able to provide higher accuracy results. This is caused by the limitation of data available for this study. Therefore, the study on breast cancer risk prediction by using ontology can be improved in the future by using broader cancer data and consistent cancer data type. 2014-03 Thesis http://eprints.utm.my/id/eprint/48058/ http://eprints.utm.my/id/eprint/48058/25/FatimatufaridahJusohMFK2014.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	RC0254 Neoplasms Tumors Oncology (including Cancer)
spellingShingle	RC0254 Neoplasms Tumors Oncology (including Cancer) Jusoh, Fatimatufaridah Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko
description	Breast cancer is a deadly disease caused by the uncontrolled growth of cells that starts in the breast. Therefore, the accurate risk prediction is crucial in assisting the selection for the suitable prevention treatment, depending on the level of the risk. However, the abundance of biomedical data from various sources creates difficulty in data organizing. In addition, the big challenge in predicting the risk of breast cancer is the different attributes of the datasets which make it inscrutable for someone who are not from the domain background. Ontology is a new method introduced to improve the knowledge discovery in complex database. Ontology approach was applied in this study to resolve this problem by providing clearer understanding of the data. In this study, ontology was also used to select important features for data analysis. Classification technique of Sequential Minimal Optimization (SMO) was also applied in this study. SMO is a fast learning algorithm of Support Vector Machine (SVM) and able to provide high accuracy results. However, the analysis of breast cancer risk shows that data analysis without ontology has slightly higher accuracy compared to data analysis with ontology, where, the first dataset is 94.7% compared to 92.1% and the accuracy for the second dataset is 96.7% compared to 96.6%. These results were different from expectation, which the application of ontology was supposed to be able to provide higher accuracy results. This is caused by the limitation of data available for this study. Therefore, the study on breast cancer risk prediction by using ontology can be improved in the future by using broader cancer data and consistent cancer data type.
format	Thesis
qualification_level	Master's degree
author	Jusoh, Fatimatufaridah
author_facet	Jusoh, Fatimatufaridah
author_sort	Jusoh, Fatimatufaridah
title	Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko
title_short	Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko
title_full	Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko
title_fullStr	Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko
title_full_unstemmed	Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko
title_sort	pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/48058/25/FatimatufaridahJusohMFK2014.pdf
_version_	1747817297552080896

Pembangunan ontologi kanser payudara bagi pemilihan data dalam meramal risiko

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