• Ms. N. Vijayalakshmi
• Divya Lakshmi

Skin disease is a major problem among people worldwide. Different machine learning techniques can be applied to identify classes of skin disease. Herein, we have applied machine learning algorithms to categorize classes of skin disease using ensemble techniques, and then a feature selection method is utilized to compare the results obtained. Machine learning algorithms are widely used in medicine. Various disease diagnosis classification algorithms have been developed to provide high accuracy for predicting disease. Many machine learning algorithms are developed for predicting various types of disease at early stages after examining the various attributes of the disease. Feature selection (FS) techniques are necessary for dealing with several dimensional datasets that may incorporate features in the high, little and, medium dimensions. In this research work, a comparative study of several filter feature selection techniques is utilized to diminish the size of the dermatology dataset. Feature Selection methods like SU, IG, CS and optimization techniques like PSO, GA and ACO have utilized and proposed a swarm based Symmetrical Uncertainty feature selection (SSU-FS) method based on SU and PSO. For evaluating the Swarm based Symmetrical Uncertainty feature selection method (SSU-FS), classification techniques like Naïve Bayes and ANN have used.

Dermatology, Data Mining, Skin, Symmetrical Uncertainty, Feature Selection, Particle Swarm Optimization, Genetic Algorithm, Ant Colony Optimization, Artificial Neural Network, Naïve Bayes, Classification.

"Artificial Neural Network for the Prediction of Dermatology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.51-57, June 2019, Available :http://www.jetir.org/papers/JETIRDB06008.pdf

"Artificial Neural Network for the Prediction of Dermatology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp51-57, June 2019, Available at : http://www.jetir.org/papers/JETIRDB06008.pdf