The week's pick
Reseach Article
Early Prevention and Detection of Skin Cancer Risk using Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 62 - Number 4 |
| Year of Publication: 2013 |
| Authors: Kawsar Ahmed, Tasnuba Jesmin, Md. Zamilur Rahman |
10.5120/10065-4662
|
Kawsar Ahmed, Tasnuba Jesmin, Md. Zamilur Rahman . Early Prevention and Detection of Skin Cancer Risk using Data Mining. International Journal of Computer Applications. 62, 4 ( January 2013), 1-6. DOI=10.5120/10065-4662
Abstract
Till now Cancer is a big question for scientific community cause of no existing treatments could solve the problems related to this dreadful disease. Research is in well progress since half century but it failed to give an accurate solution to fight against it. The development of technology in science day night tries to develop new methods of treatment. One such mile stone treatment for cancer that is giving good hope to the people is cancer treatment based on genome sequencing. With respect to Bangladesh, Skin Cancer is a fatal, deadly, disabling and costly disease whose risk is increasing at alarming rate because of unconsciousness. Like other cancer Skin Cancer also depends on some factors that are known risk factors of skin cancer. So the detection of Skin Cancer from some important risk factors is a multi-layered problem. Initially according to those risk factors 200 people's data is obtained from different diagnostic centre which contains both cancer and non-cancer patients' information and collected data is pre-processed for duplicate and missing information. After pre-processing data is clustered using K-means clustering algorithm for separating relevant and non-relevant data to Skin Cancer. Next significant frequent patterns are discovered using MAFIA algorithm shown in Table 1. Finally implement a system using Lotus Notes to predict Skin Cancer risk level with suggestions which is easier, cost reducible and time saveable.
References
- U. S. DEPARTMENT OF HEALTH AND HUMAN SERVICES, National Institutes of Health, NIH Publication No. 10-7625, 2010, pp. 1-55.
- National Library of Australia Cataloguing–in–Publication data: Lifestyle and cancer: knowledge, attitudes and behavior in NSW 2009 SHPN (CI) 120203 ISBN 978-1-74187-760-1, Published by the Cancer Institute NSW, 2012, pp. 1-29.
- Frawley and Piatetsky-Shapiro, Knowledge Discovery in Databases: An Overview. The AAAI/MIT Press, Menlo Park, C. A, 1996.
- Hian Chye Koh and Gerald Tan, "Data Mining Applications in Healthcare", Journal of Healthcare Information Management, Vol. 19, No. 2, pp. 64-72.
- Dr. Ilias Petrolias and Quan Nguyen, "Association rule tool an implementation of AprioriTID Algorithm", ID 2429851.
- Jiang Su and Harry Zhang, "A Fast Decision Tree Learning Algorithm", American Association for Artificial Intelligence, 2006
- Douglas Burdick, Manuel Calimlim and Johannes Gehrke, "MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases", Proceedings of the 17th International Conference on Data Engineering, pp. 443-452, April 02-06, 2001.
- "Skin Cancer Prevention and Early Detection", Published by American cancer society 1-800-ACS-2345, 2012, pp. 1-16.
- Zakaria Nouir, Berna Sayrac, Benoît Fourestié, Walid Tabbara, and Françoise Brouaye, "Generalization Capabilities Enhancement of a Learning System by Fuzzy Space Clustering," Journal of Communications, Vol. 2, No. 6, pp. 30-37, November 2007.
- Shantakumar B. Patil and Y. S. Kumaraswamy, "Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network", European Journal of Scientific Research ISSN 1450-216X, Vol. 31, No. 4 , pp. 642-656, Inc. 2009.
- C. Ordonez, "Programming the K-Means Clustering Algorithm in SQL," Proc. ACM Int'l Conf. Knowledge Discovery and Data Mining, pp. 823-828, 2004.
- Eric Li and Li Liu, Optimization of Frequent Itemset Mining on Multiple-Core Processor, pp. 1275-1285, 2007.
- Doug Burdick, Manuel Calimlim, Jason Flannick, Johannes Gehrke and Tomi Yiu, MAFIA: A Performance Study of Mining Maximal Frequent Itemsets, Proceedings of the 17th International Conference on Data Engineering, pp. 443-452, April 02-06, 2001.
- Shantakumar B. Patil and Y. S. Kumaraswamy, "Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network", European Journal of Scientific Research ISSN 1450-216X, Vol. 31, No. 4 , pp. 642-656, Inc. 2009.
- Anthony F. Jetant, Jennifer T. Johnson, Catherine Demastes Sheridan and Timothy J. Caffrey, "Early Detection and Treatment of Skin Cancer", American Family Physician, Vol. 62, No. 2, pp. 357-368, July 15, 2000.
- Muhammad Akmal Sapon , Khadijah Ismail and Suehazlyn Zainudin , Prediction of Diabetes by using Artificial Neural Network, 2011 International Conference on Circuits, System and Simulation IPCSIT, Vol. 7, pp. 299-303,Singapore, 2011.
- Huy Nguyen Anh Pham and Evangelos Triantaphyllou, Prediction of Diabetes by Employing a New Data Mining Approach Which Balances Fitting and Generalization , R. Lee and H. -K. Kim (Eds. ): Computer and Information Science, SCI 131, pp. 11–26, 2008.
- Manaswini Pradhan and Dr. Ranjit Kumar Sahu, "Predict the onset of diabetes disease using Artificial Neural Network (ANN)", International Journal of Computer Science & Emerging Technologies (E-ISSN: 2044-6004), pp. 303-311 Volume 2, Issue 2, April 2011.
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