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IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment

  • Systems-Level Quality Improvement
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Abstract

Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.

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Authors and Affiliations

  1. Département d’Informatique, Université du Québec à Montréal, Montréal, QC, Canada

    Moloud Abdar & Vladimir Makarenkov

  2. Department of Information Technology, Politeknik Negeri Malang, Malang, East Java, Indonesia

    Vivi Nur Wijayaningrum

  3. Dibrugarh University, Dibrugarh, Assam, India

    Sadiq Hussain

  4. Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Victoria, Australia

    Roohallah Alizadehsani

  5. Institute of Telecomputing, Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, Krakow, Poland

    Pawel Plawiak

  6. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  7. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  8. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

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  1. Moloud Abdar
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  4. Roohallah Alizadehsani
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  6. U. Rajendra Acharya
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  7. Vladimir Makarenkov
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Correspondence to Moloud Abdar.

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Conflict of interest

Author Moloud Abdar declares that he has no conflict of interest. Author Vivi Nur Wijayaningrum declares that she has no conflict of interest. Author Sadiq Hussain declares that he has no conflict of interest. Author Roohallah Alizadehsani declares that he has no conflict of interest. Author Pawel Plawiak declares that he has no conflict of interest. Author U Rajendra Acharya declares that he has no conflict of interest. Author Vladimir Makarenkov declares that he has no conflict of interest.

Ethical approval

We used two secondary datasets taken from the UCI public website (http://archive.ics.uci.edu/ml/datasets/Immunotherapy+Dataset) and (http://archive.ics.uci.edu/ml/datasets/Cryotherapy+Dataset+). No ethics approval is required for these datasets.

Animal studies

This article does not contain any studies with animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

Appendices

Appendix 1

List of Abbreviations/Symbols

Table 7 Shows the list of all Abbreviations/Symbols used in this study
Full size table

Appendix 2

Flowchart of the proposed IAPSO for AIRS

figure a

Appendix 3

Initialization of particle position

In this study, a particle consists of 9 dimensions that describe the parameters to be used in the AIRS algorithm. These values are randomly selected in different value ranges for each parameter. The range of values for each parameter used in this study are presented in Table 8:

Table 8 Mapping between the immune system and AIRS
Full size table

An example of particle initialization is shown in Table 9.

Table 9 Example of a particle
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Table 9 shows that a particle consists of 9 dimensions: affinity threshold scalar, clonal rate, hypermutation rate, mutation rate, total resources, stimulation threshold, ARB cell pool size, memory cell pool size, and k-NN with different values. These values are then used as parameters in AIRS.

Fitness value

The fitness function is used to find out how well the position has been found by each particle. A particle that has a high fitness value indicates that the position of the particle is close to the optimal solution. The higher the fitness value of a particle, the closer the particle position to the optimal solution. The fitness value can be obtained after applying the values of a particle to the parameters used in AIRS. AIRS will conduct a training process using these parameters. After the training process is done, the test process will be performed by calculating the suitability of the classification result with the actual class data. Therefore, the fitness value will be calculated using the accuracy, as shown in ACC. Moreover, the performances of the methods are evaluated using the other metrics such as precision, recall, and F-Measure, which are indicated as PRE, REC, and FM.

$$ \Big\{{\displaystyle \begin{array}{l}\mathrm{ACC}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{FP}+\mathrm{FN}+\mathrm{TN}}\&\mathrm{PRE}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}\\ {}\mathrm{REC}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}\&\mathrm{FM}=\frac{2\times \mathrm{PRE}\times \mathrm{REC}}{\mathrm{PRE}+\mathrm{REC}}\end{array}} $$

where, True Positive (TP) is the value obtained when the actual class of the data was 1 (Success) and the predicted was also 1 (Success), True Negative (TN) is the value obtained when the actual class of the data was 0 (Failed) and the predicted was 0 (Failed), False Positive (FP) is the value obtained when the actual class of the data was 0 (Failed) and the predicted was 1 (Success), False Negative (FN) is the value obtained when the actual class of the data was 1 (Success) and the predicted was 0 (Failed).

Appendix 4

Table 10 The initial results obtained by using the AIRS algorithm applied on the two original data sets
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Abdar, M., Wijayaningrum, V.N., Hussain, S. et al. IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment. J Med Syst 43, 220 (2019). https://doi.org/10.1007/s10916-019-1343-0

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