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Separation of Different Blogs from Skin Disease Data using Artificial Intelligence
A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9427218/ https://www.ncbi.nlm.nih.gov/pubmed/36052051 http://dx.doi.org/10.1155/2022/7538643 |
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author | Abdulaal, Mohammed J. Mehedi, Ibrahim M. Aljohani, Abdulah Jeza Milyani, Ahmad H. Mahmoud, Mohamed Abusorrah, Abdullah M. Jannat, Rahtul |
author_facet | Abdulaal, Mohammed J. Mehedi, Ibrahim M. Aljohani, Abdulah Jeza Milyani, Ahmad H. Mahmoud, Mohamed Abusorrah, Abdullah M. Jannat, Rahtul |
author_sort | Abdulaal, Mohammed J. |
collection | PubMed |
description | A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them. |
format | Online Article Text |
id | pubmed-9427218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94272182022-08-31 Separation of Different Blogs from Skin Disease Data using Artificial Intelligence Abdulaal, Mohammed J. Mehedi, Ibrahim M. Aljohani, Abdulah Jeza Milyani, Ahmad H. Mahmoud, Mohamed Abusorrah, Abdullah M. Jannat, Rahtul Comput Intell Neurosci Research Article A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them. Hindawi 2022-08-23 /pmc/articles/PMC9427218/ /pubmed/36052051 http://dx.doi.org/10.1155/2022/7538643 Text en Copyright © 2022 Mohammed J. Abdulaal et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Abdulaal, Mohammed J. Mehedi, Ibrahim M. Aljohani, Abdulah Jeza Milyani, Ahmad H. Mahmoud, Mohamed Abusorrah, Abdullah M. Jannat, Rahtul Separation of Different Blogs from Skin Disease Data using Artificial Intelligence |
title | Separation of Different Blogs from Skin Disease Data using Artificial Intelligence |
title_full | Separation of Different Blogs from Skin Disease Data using Artificial Intelligence |
title_fullStr | Separation of Different Blogs from Skin Disease Data using Artificial Intelligence |
title_full_unstemmed | Separation of Different Blogs from Skin Disease Data using Artificial Intelligence |
title_short | Separation of Different Blogs from Skin Disease Data using Artificial Intelligence |
title_sort | separation of different blogs from skin disease data using artificial intelligence |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9427218/ https://www.ncbi.nlm.nih.gov/pubmed/36052051 http://dx.doi.org/10.1155/2022/7538643 |
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