A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Barati, E; Saraee, MH; Mohammadi, A; Adibi, N; Ahmadzadeh, MR

Abstract

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data.

Citation

Barati, E., Saraee, M., Mohammadi, A., Adibi, N., & Ahmadzadeh, M. (2011). A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

• Journal Article Type: Article
• Publication Date: Mar 1, 2011
• Deposit Date: Oct 21, 2011
• Publicly Available Date: Apr 5, 2016
• Journal: Journal of Selected Areas in Health Informatics (JSHI)
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Issue: 3
• Pages: 1-11
• Publisher URL: http://www.cyberjournals.com/Papers/Mar2011/11.pdf
• Related Public URLs: http://www.cyberjournals.com/Mar2011.html