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The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal
BACKGROUND: Warts can be extremely painful conditions that may be associated with localised bleeding and discharge. They are commonly treated by cryotherapy or immunotherapy. However, each of these therapies have discomforting side effects and are no official dermatological guideline that exist that...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Dermatological Association; The Korean Society for Investigative Dermatology
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273329/ https://www.ncbi.nlm.nih.gov/pubmed/34341636 http://dx.doi.org/10.5021/ad.2021.33.4.345 |
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author | Singh, Yashik |
author_facet | Singh, Yashik |
author_sort | Singh, Yashik |
collection | PubMed |
description | BACKGROUND: Warts can be extremely painful conditions that may be associated with localised bleeding and discharge. They are commonly treated by cryotherapy or immunotherapy. However, each of these therapies have discomforting side effects and are no official dermatological guideline that exist that may be used to determine which of these methods would work on an individual patient. OBJECTIVE: This study aimed at developing a machine learning algorithm that improved the prediction of the outcome of wart removing using cryotherapy and immunotherapy. METHODS: Support vector machines, core vector machines, random forest, k-nearest neighbours, multilayer perceptron and binary logistic regression was applied on datasets in to create a model that predicted the outcome of an immunotherapy and cryotherapy treatments based on sex, age, time that has passed since last treatment, number of warts, type, area, diameter and result of treatment. RESULTS: The average accuracy of the immunotherapy prediction was 88.6%±8.0% while the same measure for cryotherapy prediction was 94.6%±4.0%. The most efficient immunotherapy and cryotherapy model had an accuracy of 100%, predicating the correct treatment outcome when applied to all test cases. CONCLUSION: This study successfully created a machine learning model that improved the prediction ability of the outcome of immunotherapy and cryotherapy for wart removal. This model created a more in-depth guideline for understanding is immunotherapy would work and took a new approach to cryotherapy. |
format | Online Article Text |
id | pubmed-8273329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Korean Dermatological Association; The Korean Society for Investigative Dermatology |
record_format | MEDLINE/PubMed |
spelling | pubmed-82733292021-08-01 The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal Singh, Yashik Ann Dermatol Original Article BACKGROUND: Warts can be extremely painful conditions that may be associated with localised bleeding and discharge. They are commonly treated by cryotherapy or immunotherapy. However, each of these therapies have discomforting side effects and are no official dermatological guideline that exist that may be used to determine which of these methods would work on an individual patient. OBJECTIVE: This study aimed at developing a machine learning algorithm that improved the prediction of the outcome of wart removing using cryotherapy and immunotherapy. METHODS: Support vector machines, core vector machines, random forest, k-nearest neighbours, multilayer perceptron and binary logistic regression was applied on datasets in to create a model that predicted the outcome of an immunotherapy and cryotherapy treatments based on sex, age, time that has passed since last treatment, number of warts, type, area, diameter and result of treatment. RESULTS: The average accuracy of the immunotherapy prediction was 88.6%±8.0% while the same measure for cryotherapy prediction was 94.6%±4.0%. The most efficient immunotherapy and cryotherapy model had an accuracy of 100%, predicating the correct treatment outcome when applied to all test cases. CONCLUSION: This study successfully created a machine learning model that improved the prediction ability of the outcome of immunotherapy and cryotherapy for wart removal. This model created a more in-depth guideline for understanding is immunotherapy would work and took a new approach to cryotherapy. The Korean Dermatological Association; The Korean Society for Investigative Dermatology 2021-08 2021-07-01 /pmc/articles/PMC8273329/ /pubmed/34341636 http://dx.doi.org/10.5021/ad.2021.33.4.345 Text en Copyright © 2021 The Korean Dermatological Association and The Korean Society for Investigative Dermatology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Singh, Yashik The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal |
title | The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal |
title_full | The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal |
title_fullStr | The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal |
title_full_unstemmed | The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal |
title_short | The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal |
title_sort | application of machine learning in predicting outcome of cryotherapy and immunotherapy for wart removal |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273329/ https://www.ncbi.nlm.nih.gov/pubmed/34341636 http://dx.doi.org/10.5021/ad.2021.33.4.345 |
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