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Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis

The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not c...

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Autores principales: Uzun Ozsahin, Dilber, Onakpojeruo, Efe Precious, Uzun, Berna, Mustapha, Mubarak Taiwo, Ozsahin, Ilker
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955898/
https://www.ncbi.nlm.nih.gov/pubmed/36832106
http://dx.doi.org/10.3390/diagnostics13040618
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author Uzun Ozsahin, Dilber
Onakpojeruo, Efe Precious
Uzun, Berna
Mustapha, Mubarak Taiwo
Ozsahin, Ilker
author_facet Uzun Ozsahin, Dilber
Onakpojeruo, Efe Precious
Uzun, Berna
Mustapha, Mubarak Taiwo
Ozsahin, Ilker
author_sort Uzun Ozsahin, Dilber
collection PubMed
description The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of −0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors.
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spelling pubmed-99558982023-02-25 Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis Uzun Ozsahin, Dilber Onakpojeruo, Efe Precious Uzun, Berna Mustapha, Mubarak Taiwo Ozsahin, Ilker Diagnostics (Basel) Article The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of −0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors. MDPI 2023-02-08 /pmc/articles/PMC9955898/ /pubmed/36832106 http://dx.doi.org/10.3390/diagnostics13040618 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Uzun Ozsahin, Dilber
Onakpojeruo, Efe Precious
Uzun, Berna
Mustapha, Mubarak Taiwo
Ozsahin, Ilker
Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis
title Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis
title_full Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis
title_fullStr Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis
title_full_unstemmed Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis
title_short Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis
title_sort mathematical assessment of machine learning models used for brain tumor diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955898/
https://www.ncbi.nlm.nih.gov/pubmed/36832106
http://dx.doi.org/10.3390/diagnostics13040618
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