Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

Nagaraj P,1 Deepalakshmi P,1 Romany F Mansour,2 Ahmed Almazroa3 1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India; 2Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egy...

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Autores principales:	P N, P D, Mansour RF, Almazroa A
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	diabetes gbt feature selection artificial flora classification Specialties of internal medicine RC581-951
Acceso en línea:	https://doaj.org/article/857f89f735d34f5a81ccd8504032b61c
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spelling	oai:doaj.org-article:857f89f735d34f5a81ccd8504032b61c2021-12-02T16:07:16ZArtificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification1178-7007https://doaj.org/article/857f89f735d34f5a81ccd8504032b61c2021-06-01T00:00:00Zhttps://www.dovepress.com/artificial-flora-algorithm-based-feature-selection-with-gradient-boost-peer-reviewed-fulltext-article-DMSOhttps://doaj.org/toc/1178-7007Nagaraj P,1 Deepalakshmi P,1 Romany F Mansour,2 Ahmed Almazroa3 1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India; 2Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt; 3Department of imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi ArabiaCorrespondence: Nagaraj PDepartment of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil, Srivilliputtur, Virudhunagar, Tamil Nadu, 626126, IndiaEmail nagaraj.p@klu.ac.inPurpose: Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes.Methods: Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients’ electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients’ cases to type I, type II, or gestational diabetes mellitus.Results: The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%.Conclusion: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.Keywords: diabetes, GBT, feature selection, artificial flora, classificationP NP DMansour RFAlmazroa ADove Medical Pressarticlediabetesgbtfeature selectionartificial floraclassificationSpecialties of internal medicineRC581-951ENDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Vol Volume 14, Pp 2789-2806 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	diabetes gbt feature selection artificial flora classification Specialties of internal medicine RC581-951
spellingShingle	diabetes gbt feature selection artificial flora classification Specialties of internal medicine RC581-951 P N P D Mansour RF Almazroa A Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification
description	Nagaraj P,1 Deepalakshmi P,1 Romany F Mansour,2 Ahmed Almazroa3 1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India; 2Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt; 3Department of imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi ArabiaCorrespondence: Nagaraj PDepartment of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil, Srivilliputtur, Virudhunagar, Tamil Nadu, 626126, IndiaEmail nagaraj.p@klu.ac.inPurpose: Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes.Methods: Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients’ electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients’ cases to type I, type II, or gestational diabetes mellitus.Results: The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%.Conclusion: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.Keywords: diabetes, GBT, feature selection, artificial flora, classification
format	article
author	P N P D Mansour RF Almazroa A
author_facet	P N P D Mansour RF Almazroa A
author_sort	P N
title	Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification
title_short	Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification
title_full	Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification
title_fullStr	Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification
title_full_unstemmed	Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification
title_sort	artificial flora algorithm-based feature selection with gradient boosted tree model for diabetes classification
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/857f89f735d34f5a81ccd8504032b61c
work_keys_str_mv	AT pn artificialfloraalgorithmbasedfeatureselectionwithgradientboostedtreemodelfordiabetesclassification AT pd artificialfloraalgorithmbasedfeatureselectionwithgradientboostedtreemodelfordiabetesclassification AT mansourrf artificialfloraalgorithmbasedfeatureselectionwithgradientboostedtreemodelfordiabetesclassification AT almazroaa artificialfloraalgorithmbasedfeatureselectionwithgradientboostedtreemodelfordiabetesclassification
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Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

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