Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management

The fourth most frequent cause of cancer death in women is cervical cancer. No sign can be observed in the early stages of the disease. In addition, cervical cancer diagnosis methods used in health centers are time consuming and costly. Data classification has been widely applied in diagnosis c...

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Main Authors: Wahid, Juliana, Al-Mazini, Hassan Fouad Abbas
Format: Conference or Workshop Item
Language:English
Published: 2018
Subjects:
Online Access:https://repo.uum.edu.my/id/eprint/25261/1/KMICE%202018%20393%20397.pdf
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author Wahid, Juliana
Al-Mazini, Hassan Fouad Abbas
author_facet Wahid, Juliana
Al-Mazini, Hassan Fouad Abbas
author_sort Wahid, Juliana
collection UUM
description The fourth most frequent cause of cancer death in women is cervical cancer. No sign can be observed in the early stages of the disease. In addition, cervical cancer diagnosis methods used in health centers are time consuming and costly. Data classification has been widely applied in diagnosis cervical cancer for knowledge acquisition. However, none of existing intelligent methods is comprehensible, and they look like a black box to clinicians. In this paper, an ant colony optimization based classification algorithm, Ant-Miner is applied to analyze the cervical cancer data set. The cervical cancer data set used was obtained from the repository of the University of California, Irvine. The proposed algorithm outperforms the previous approach, support vector machine, in the same domain, in terms of better result of classification accuracy.
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spelling uum-252612018-11-28T01:13:23Z https://repo.uum.edu.my/id/eprint/25261/ Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management Wahid, Juliana Al-Mazini, Hassan Fouad Abbas QA75 Electronic computers. Computer science The fourth most frequent cause of cancer death in women is cervical cancer. No sign can be observed in the early stages of the disease. In addition, cervical cancer diagnosis methods used in health centers are time consuming and costly. Data classification has been widely applied in diagnosis cervical cancer for knowledge acquisition. However, none of existing intelligent methods is comprehensible, and they look like a black box to clinicians. In this paper, an ant colony optimization based classification algorithm, Ant-Miner is applied to analyze the cervical cancer data set. The cervical cancer data set used was obtained from the repository of the University of California, Irvine. The proposed algorithm outperforms the previous approach, support vector machine, in the same domain, in terms of better result of classification accuracy. 2018-07-25 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/25261/1/KMICE%202018%20393%20397.pdf Wahid, Juliana and Al-Mazini, Hassan Fouad Abbas (2018) Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management. In: Knowledge Management International Conference (KMICe) 2018, 25 –27 July 2018, Miri Sarawak, Malaysia. http://www.kmice.cms.net.my/ProcKMICe/KMICe2018/toc.html
spellingShingle QA75 Electronic computers. Computer science
Wahid, Juliana
Al-Mazini, Hassan Fouad Abbas
Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management
title Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management
title_full Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management
title_fullStr Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management
title_full_unstemmed Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management
title_short Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management
title_sort classification of cervical cancer using ant miner for medical expertise knowledge management
topic QA75 Electronic computers. Computer science
url https://repo.uum.edu.my/id/eprint/25261/1/KMICE%202018%20393%20397.pdf
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