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

• Main Authors: Wahid, Juliana, Al-Mazini, Hassan Fouad Abbas
• Format: Conference or Workshop Item
• Language: English
• Published: 2018
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/25261/1/KMICE%202018%20393%20397.pdf; http://repo.uum.edu.my/25261/; http://www.kmice.cms.net.my/ProcKMICe/KMICe2018/toc.html

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.