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Recommender System for Prevention of Juvenile Plantar Dermatosis Disease

L. Arockiam, S. Charles, C. Lalitha, A. Sujanitha, V. Arul Kumar

Abstract


Juvenile Plantar Dermatosis (JPD) known as sweaty sock syndrome. It affects the skin, which becomes scaly and red on the soles of the feet of the children and the young teenagers. A new framework is developed to provide an offline solution for JPD using descriptive and predictive modeling. The symptoms and stages of JPD are classified using clustering and decision tree technique. In clustering technique, the patients’ objects are categorized using symptoms. The decision tree method has been employed for finding the decision rule based on symptoms, which is used for recommendation. The recommendations are useful to the patients those who are affected by the JPD disease.

Keywords


K-means, Clustering, JPD, Decision Tree.

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References


P. K. Chan, M. D. F. Schlag, and J. Y. Zien,“ Spectral k-way ratio-cut partitioning and clustering”. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 13(9):1088–1096, 1994.

Gibbs NF, “Juvenile plantar dermatitis. Can sweat cause foot, rash and peeling”, Postgrad Med 2004;115:73-5

S-H.Min, I.Han: Optimizing Collaborative Filtering Recommender Systems.Lecture Notes in Artificial Intelligence vol. 3528, 2005, pp.313–319

Kamal Jeet Brar, S. D. Shenoi, C. Balachandran, Vandana Rai Mehta, “Clinical profile of forefoot eczema: A study of 42 cases”, Indian J Dermatol Venereol Leprol May-June 2005 Vol 71 Issue 3.

Feramisco JD, Sadreyev RI, Murray ML, Grishin NV, Tsao H, “Phenotypic and genotypic analyses of genetic skin disease through the Online endelian Inheritance in Man (OMIM) database”, J Invest Dermatol. 2009 Nov; 129 (11):2628-36. Epub 2009, Jun 18.

P.Kiran Sree, I Ramesh Babu, “Face Detection from still and Video Images using Unsupervised Cellular Automata with K-means clustering algorithm”, ICGST-GVIP Journal, ISSN: 1687-398X, Volume 8, Issue 2, July 2008.

Liu B, Xia Y, Yu P, “Clustering through decision tree construction”, In Proceedings of the ACM International Conference on Information and Knowledge Management Washington, DC; 2000.

Koundourakis G, En Visioner, “A Data Mining Framework Based On Decision Trees”. Doctoral Thesis University of Manchester Institute of Science and Technology; 2001.

Stasis A, Skarpalezos D, Pavlopoulos S, Koutsouris D, ”Differentiation of opening snap, second heart sound split and third heart sound, using a multiple Decision Tree Architecture”, Computational Management Science Conference Crete, Journal of the CMS, 2003.

Tang, Y. T. and McCalla, G. (2003b) Mining the implicit ratings for focused collaborative filtering for paper recommendations. To appear, in Workshop on User and Group Models for Web-based Adaptive Collaborative Environments, 9th International Conference on User Modeling (UM 2003), June 2003, Johnstown, U.S.A. 2003.

Han J, Kamber M, “Data Mining: Concepts and Techniques”, Morgan Kaufman Publisher; 2001.

Stasis A, “Decision Support System for Heart Sound Diagnosis, using digital signal processing algorithms and data mining techniques”. PhD Thesis Athens, National Technical University of Athens; 2003.

Van Diggelen MW, Van Dijk E, Hausman R, ”The enigma of juvenile plantar dermatosis”, Am J Dermatopathol 1986;8:336-40.


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