Classification of Skin Disease using Ensemble Data Mining Techniques

Document Type: Research Articles


Research Scholor, MCA Department, VBS Purvanchal University, Jaunpur, India.


Objective: Skin diseases are a major global health problem associated with high number of people. With the rapid
development of technologies and the application of various data mining techniques in recent years, the progress of
dermatological predictive classification has become more and more predictive and accurate. Therefore, development of
machine learning techniques, which can effectively differentiate skin disease classification, is of vast importance. The
machine learning techniques applied to skin disease prediction so far, no techniques outperforms over all the others.
Methods: In this research paper, we present a new method, which applies five different data mining techniques and
then developed an ensemble approach that consists all the five different data mining techniques as a single unit. We
use informative Dermatology data to analysis different data mining techniques to classify the skin disease and then, an
ensemble machine learning method is applied. Results: The proposed ensemble method, which is based on machine
learning was tested on Dermatology datasets and classify the type of skin disease in six different classes like include
C1: psoriasis, C2: seborrheic dermatitis, C3: lichen planus, C4: pityriasis rosea, C5: chronic dermatitis, C6: pityriasis
rubra. The results show that the dermatological prediction accuracy of the test data set is increased compared to a single
classifier. Conclusion: The ensemble method used on Dermatology datasets give better performance as compared to
different classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.


Main Subjects