Cancer recurrence prediction using machine learning
Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed wit...
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| Format: | Article |
| Language: | English |
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AIRCC Publishing Corporation
2014
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| Online Access: | http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/1/2214ijcsity02.pdf |
| _version_ | 1848781471061377024 |
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| author | Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali |
| author_facet | Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali |
| author_sort | Shoon Lei, Win |
| building | IIUM Repository |
| collection | Online Access |
| description | Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not very reliable. The microarray gene expression technology is a promising technology that could predict cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the computational complexity, we employ an entropy-based gene selection approach to select relevant prognostic genes that are directly responsible for recurrence prediction. This proposed system has achieved an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results demonstrate the efficacy of our framework. |
| first_indexed | 2025-11-14T15:50:05Z |
| format | Article |
| id | iium-37773 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:50:05Z |
| publishDate | 2014 |
| publisher | AIRCC Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-377732018-06-12T02:32:50Z http://irep.iium.edu.my/37773/ Cancer recurrence prediction using machine learning Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali Q Science (General) Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not very reliable. The microarray gene expression technology is a promising technology that could predict cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the computational complexity, we employ an entropy-based gene selection approach to select relevant prognostic genes that are directly responsible for recurrence prediction. This proposed system has achieved an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results demonstrate the efficacy of our framework. AIRCC Publishing Corporation 2014-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37773/1/2214ijcsity02.pdf Shoon Lei, Win and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah and Noorbatcha, Ibrahim Ali (2014) Cancer recurrence prediction using machine learning. International Journal of Computational Science and Information Technology (IJCSITY), 2 (2). pp. 11-20. ISSN 2320-8457 (P) 2320-7442 (O) http://airccse.org/journal/ijcsity/Current2014.html 10.5121/ijcsity.2014.2202 |
| spellingShingle | Q Science (General) Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali Cancer recurrence prediction using machine learning |
| title | Cancer recurrence prediction using machine learning |
| title_full | Cancer recurrence prediction using machine learning |
| title_fullStr | Cancer recurrence prediction using machine learning |
| title_full_unstemmed | Cancer recurrence prediction using machine learning |
| title_short | Cancer recurrence prediction using machine learning |
| title_sort | cancer recurrence prediction using machine learning |
| topic | Q Science (General) |
| url | http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/1/2214ijcsity02.pdf |