Title: Machine learning algorithms using binary classification and multi model ensemble techniques for skin diseases prediction

Authors: Vikas Chaurasia; Saurabh Pal

Addresses: Department of Computer Applications, VBS Purvanchal University, Jaunpur, UP, India ' Department of Computer Applications, VBS Purvanchal University, Jaunpur, UP, India

Abstract: Skin disease has more touchiness as compared to any other disease. Regular skin issues are dermatitis. The main focus of this research paper will be on dermatology database which contains different eryhemato-squamous diseases class as psoriasis, seboreic dermatitis, lichen planus, pityriasisrosea, cronic dermatitis and pityriasisrubrapilaris. Each record is a collection of 33 attributes which are linear values and one attribute of them is nominal. The 75% of the dataset utilise for demonstrating and keep down 25% for approval. The purpose of this article is to achieve the best-performing classifier that will communicate in the collection of dermatological information. Therefore, k-nearest neighbours and support vector machines are used. By using ten-fold cross validation and assess calculations utilising the accuracy metric. This is a gross metric which will prove the developed model is best one.

Keywords: eryhemato-squamous; k-nearest neighbours; KNN; classification and regression trees; CARTs; support vector machines; SVMs; ensemble methods.

DOI: 10.1504/IJBET.2020.110361

International Journal of Biomedical Engineering and Technology, 2020 Vol.34 No.1, pp.57 - 74

Received: 18 May 2019
Accepted: 31 Mar 2020
Published online: 15 Oct 2020 *

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