Subscribe Now Subscribe Today
Science Alert
Curve Top
Journal of Artificial Intelligence
  Year: 2013 | Volume: 6 | Issue: 4 | Page No.: 245-256
DOI: 10.3923/jai.2013.245.256
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Design of Automatic Detection of Erythemato-squamous Diseases Through Threshold-based ABC-FELM Algorithm

N. Badrinath, G. Gopinath and K.S. Ravichandran

This study proposes hybrid techniques which are based on Artificial Bee Colony (ABC) algorithm for data preprocessing and Fuzzy Extreme Learning Machine (FELM) classifier for an automatic detection of the Erythemato-Squamous Diseases (ESD). This ESDs require huge computational efforts to predict the diseases because almost all the six ESD diseases namely, psoriasis, lichen planus, seboreic dermatitis, pityriasis rubra pilaris, chronic dermatitis and pityriasis rosea have common features for more than 90%. In the recent survey it has been highlighted that, there are many machine learning algorithms performing better than the conventional techniques. In this study, we propose threshold based ABC-FELM algorithm which is used for both future extraction as well as the classifier to enhance the accuracy of the prediction of ESDs and computational time. Moreover, this hybrid mechanism is implemented and tested with 366 original patients’ datasets. The threshold based data preprocessing reduces the dimensionality of the datasets considerably and hence it improves the time complexity. Finally, the proposed methodologies proved to be a potential solution for the diagnosis of ESD with significant improvement in computational time, namely less than 1 second and the accuracy (99.57%) compared to other models discussed in the recent literature.
PDF Fulltext XML References Citation Report Citation
How to cite this article:

N. Badrinath, G. Gopinath and K.S. Ravichandran, 2013. Design of Automatic Detection of Erythemato-squamous Diseases Through Threshold-based ABC-FELM Algorithm. Journal of Artificial Intelligence, 6: 245-256.

DOI: 10.3923/jai.2013.245.256






Curve Bottom