| ชื่อเรื่อง | : | Significant cancer risk factor extraction : an association rule discovery approach |
| นักวิจัย | : | Nahar, Jesmin. , Tickle, Kevin. |
| คำค้น | : | Applied research. , 899999 Information and Communication Services not elsewhere classified. , 080109 Pattern Recognition and Data Mining. , Algorithms. , Cancer , Cancer , Cancer Risk Factor |
| หน่วยงาน | : | Central Queensland University, Australia |
| ผู้ร่วมงาน | : | - |
| ปีพิมพ์ | : | 2551 |
| อ้างอิง | : | http://hdl.cqu.edu.au/10018/28631 , http://dx.doi.org/10.1109/ICCITECHN.2008.4803102 , cqu:4395 |
| ที่มา | : | Nahar, J and Tickle, K 2008, 'Significant cancer risk factor extraction: an association rule discovery approach', IEEE International Workshop on Data Mining and Artificial Intelligence (DMAI'08) in conjunction with 11th IEEE International Conference on Computer and Information Technology (ICCIT 2008), 25-27 December 2008, Khulna, Bangladesh pp. 108-114. http://dx.doi.org/10.1109/ICCITECHN.2008.4803102 |
| ความเชี่ยวชาญ | : | - |
| ความสัมพันธ์ | : | Proceedings of IEEE International Workshop on Data Mining and Artificial Intelligence in conjunction with 11th International Conference on Computer and Information Technology (ICCIT 2008), 25-27th December, 2008, Khulna, Bangladesh. USA. : IEEE, 2008. p. 108-114 7 pages Refereed 9781424421367 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository. |
| ขอบเขตของเนื้อหา | : | - |
| บทคัดย่อ/คำอธิบาย | : | Cancer is the top most death threat for human life all over the world. Current research in the cancer area is still struggling to provide better support to a cancer patient. In this research our aim is to identify the significant risk factors for particular types of cancer. First, we constructed a risk factor data set through an extensive literature review of bladder, breast, cervical, lung, prostate and skin cancer. We further employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithm in order to discover most significant risk factors for particular types ofcancer. Discovery risk factor has been identified to shows highest confidence values. We concluded that apriori indicates to be the best association rule-mining algorithm for significant risk factor discovery. |
| บรรณานุกรม | : |
Nahar, Jesmin. , Tickle, Kevin. . (2551). Significant cancer risk factor extraction : an association rule discovery approach.
กรุงเทพมหานคร : Central Queensland University, Australia. Nahar, Jesmin. , Tickle, Kevin. . 2551. "Significant cancer risk factor extraction : an association rule discovery approach".
กรุงเทพมหานคร : Central Queensland University, Australia. Nahar, Jesmin. , Tickle, Kevin. . "Significant cancer risk factor extraction : an association rule discovery approach."
กรุงเทพมหานคร : Central Queensland University, Australia, 2551. Print. Nahar, Jesmin. , Tickle, Kevin. . Significant cancer risk factor extraction : an association rule discovery approach. กรุงเทพมหานคร : Central Queensland University, Australia; 2551.
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