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Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method

Applied Biochemistry and Biotechnology volume 191, pages 637–656 (2020)Cite this article

Abstract

Skin disease is the most common problem between people. Due to pollution and deployment of ozone layer, harmful UV rays of sun burn the skin and develop various types of skin diseases. Nowadays, machine learning and deep learning algorithms are generally used for diagnosis for various kinds of diseases. In this study, we have applied three feature extraction techniques univariate feature selection, feature importance, and correlation matrix with heat map to find the optimum data subset of erythemato-squamous disease. Four classification techniques Gaussian Naïve Bayesian (NB), decision tree (DT), support vector machine (SVM), and random forest are used for measuring the performance of model. Stacking ensemble technique is then applied to enhance the prediction performance of the model. The proposed method used for measuring the performance of the model. It is finding that the optimal subset of the erythemato-squamous disease is performed well in the case of correlation and heat map feature selection techniques. The mean value, slandered deviation, root mean square error, kappa statistical error, and area under receiver operating characteristics and accuracy are calculated for demonstrating the effectiveness of the proposed model. The feature selection techniques applied with staking ensemble technique gives the better result as compared to individual machine learning techniques. The obtained results show that the performance of proposed model is higher than previous results obtained by researchers.

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Data Availability

The datasets used in this paper are publicly online available as described in “Dataset Analysis” section.

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Author information

Affiliations

  1. Research Scholar, MCA Department, VBS Purvanchal University, Jaunpur, India

    Anurag Kumar Verma

  2. Department of MCA, VBS Purvanchal University, Jaunpur, UP, India

    Saurabh Pal

Authors
  1. Anurag Kumar Verma
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  2. Saurabh Pal
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Contributions

All authors contributed both the concepts and contents of this study. AKV provided the manuscript under supervised by SP. All authors also performed discussion intensively for contents improvement. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Saurabh Pal.

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Verma, A.K., Pal, S. Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method. Appl Biochem Biotechnol 191, 637–656 (2020). https://doi.org/10.1007/s12010-019-03222-8

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  • DOI: https://doi.org/10.1007/s12010-019-03222-8

Keywords

  • Erythemato-squamous disease
  • SVM
  • Stacking
  • RMSE
  • KSE