Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization

Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which...

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Vydané v:	Diagnostics (Basel) Ročník 13; číslo 12; s. 2038
Hlavní autori:	Alhussan, Amel Ali, Abdelhamid, Abdelaziz A., Towfek, S. K., Ibrahim, Abdelhameed, Eid, Marwa M., Khafaga, Doaa Sami, Saraya, Mohamed S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 01.06.2023 MDPI
Predmet:	Accuracy Al-Biruni earth radius optimization Algorithms Chronic illnesses Classification Datasets Decision trees diabetes dipper throated optimization Disease Epidemiology Feature selection Forecasts and trends Gestational diabetes Hyperglycemia Insulin resistance Literature reviews Machine learning Mathematical optimization Medical research Medicine, Experimental Neural networks Optimization algorithms Optimization techniques Public health random forest Risk factors Support vector machines Type 2 diabetes random forest machine learning dipper throated optimization diabetes feature selection Al-Biruni earth radius optimization
ISSN:	2075-4418, 2075-4418
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Abstract	Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.
AbstractList	Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods. In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods. In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures.INTRODUCTIONIn public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures.In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO.METHODOLOGYIn this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO.The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests.RESULTSThe proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests.The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.CONCLUSIONSThe results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.
Audience	Academic
Author	Alhussan, Amel Ali Ibrahim, Abdelhameed Saraya, Mohamed S. Eid, Marwa M. Abdelhamid, Abdelaziz A. Khafaga, Doaa Sami Towfek, S. K.
AuthorAffiliation	1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; aaalhussan@pnu.edu.sa 2 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia 5 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt 7 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt; mmm@ieee.org 4 Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA; sktowfek@jcsis.org 6 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt; abdelhameedibrahim79@gmail.com (A.I.); mohamedsabry83@mans.edu.eg (M.S.S.) 3 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
AuthorAffiliation_xml	– name: 4 Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA; sktowfek@jcsis.org – name: 6 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt; abdelhameedibrahim79@gmail.com (A.I.); mohamedsabry83@mans.edu.eg (M.S.S.) – name: 1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; aaalhussan@pnu.edu.sa – name: 7 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt; mmm@ieee.org – name: 5 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt – name: 2 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia – name: 3 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Author_xml	– sequence: 1 givenname: Amel Ali orcidid: 0000-0001-7530-7961 surname: Alhussan fullname: Alhussan, Amel Ali – sequence: 2 givenname: Abdelaziz A. orcidid: 0000-0001-7080-1979 surname: Abdelhamid fullname: Abdelhamid, Abdelaziz A. – sequence: 3 givenname: S. K. surname: Towfek fullname: Towfek, S. K. – sequence: 4 givenname: Abdelhameed orcidid: 0000-0002-8352-6731 surname: Ibrahim fullname: Ibrahim, Abdelhameed – sequence: 5 givenname: Marwa M. surname: Eid fullname: Eid, Marwa M. – sequence: 6 givenname: Doaa Sami orcidid: 0000-0002-9843-6392 surname: Khafaga fullname: Khafaga, Doaa Sami – sequence: 7 givenname: Mohamed S. surname: Saraya fullname: Saraya, Mohamed S.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37370932$$D View this record in MEDLINE/PubMed
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Snippet	Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus,... In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has...
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SubjectTerms	Accuracy Al-Biruni earth radius optimization Algorithms Chronic illnesses Classification Datasets Decision trees diabetes dipper throated optimization Disease Epidemiology Feature selection Forecasts and trends Gestational diabetes Hyperglycemia Insulin resistance Literature reviews Machine learning Mathematical optimization Medical research Medicine, Experimental Neural networks Optimization algorithms Optimization techniques Public health random forest Risk factors Support vector machines Type 2 diabetes
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Title	Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization
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