Use of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries

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dc.citation.epage645
dc.citation.issue4
dc.citation.spage638
dc.contributor.affiliationУніверситет Мохаммеда Першого
dc.contributor.affiliationЕміратський авіаційний університет
dc.contributor.affiliationUniversity Mohammed First
dc.contributor.affiliationEmirates Aviation University
dc.contributor.authorБутаеб, В.
dc.contributor.authorБадауї, М.
dc.contributor.authorАль Алі, Х.
dc.contributor.authorБутаеб, А.
dc.contributor.authorЛамлілі, М.
dc.contributor.authorBoutayeb, W.
dc.contributor.authorBadaoui, M.
dc.contributor.authorAl Ali, H.
dc.contributor.authorBoutayeb, A.
dc.contributor.authorLamlili, M.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-11-01T07:49:46Z
dc.date.available2023-11-01T07:49:46Z
dc.date.created2021-03-01
dc.date.issued2021-03-01
dc.description.abstractПоширеність діабету в країнах Перської затоки значно зростає через різні фактори ризику, такі як: ожиріння, нездорове харчування, фізична бездіяльність та куріння. Метою цього дослідження є використання засобів добування даних та інтелектуального аналізу даних для визначення різних факторів ризику розвитку цукрового діабету другого типу (ЦД2) у країнах Перської затоки на основі бази даних Gulf COAST.
dc.description.abstractPrevalence of diabetes in Gulf countries is knowing a significant increase because of various risk factors, such as: obesity, unhealthy diet, physical inactivity and smoking. The aim of our proposed study is to use Data Mining and Data Analysis tools in order to determine different risk factors of the development of Type 2 diabetes mellitus (T2DM) in Gulf countries, from Gulf COAST dataset.
dc.format.extent638-645
dc.format.pages8
dc.identifier.citationUse of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries / W. Boutayeb, M. Badaoui, H. Al Ali, A. Boutayeb, M. Lamlili // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 4. — P. 638–645.
dc.identifier.citationenUse of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries / W. Boutayeb, M. Badaoui, H. Al Ali, A. Boutayeb, M. Lamlili // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 4. — P. 638–645.
dc.identifier.doi10.23939/mmc2021.04.638
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/60453
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofMathematical Modeling and Computing, 4 (8), 2021
dc.relation.references[1] Definition, diagnosis and classification of diabetes mellitus and its complications: report of a WHO consultation. World Health Organization (1999).
dc.relation.references[2] Meo S. A., Usmani A. M., Qalbani E. Prevalence of type 2 diabetes in the Arab world: impact of GDP and energy consumption. Eur. Rev. Med. Pharmacol. Sci. 21 (6), 1303–1312 (2017).
dc.relation.references[3] Boutayeb W., Lamlili M., Boutayeb A., Derouich M. Mathematical modelling and simulation of β-cell mass, insulin and glucose dynamics: Effect of genetic predisposition to diabetes. Journal of Biomedical Science and Engineering. 7 (6), 330–342 (2014).
dc.relation.references[4] Elhayany A., Lustman A., Abel R., Attal-Singer J., Vinker S. A low carbohydrate Mediterranean diet improves cardiovascular risk factors and diabetes control among overweight patients with type 2 diabetes mellitus: a 1-year prospective randomized intervention study. Diabetes, Obesity and Metabolism. 12 (3), 204–209 (2010).
dc.relation.references[5] Pulgaron E. R., Delamater A. M. Obesity and type 2 diabetes in children: epidemiology and treatment. Current Diabetes Reports. 14 (8), Article number: 508 (2014).
dc.relation.references[6] Donaghue K. C., Chiarelli F., Trotta D., Allgrove J., Dahl-Jorgensen Knut. Microvascular and macrovascular complications. Pediatric Diabetes. 8, 163–170 (2007).
dc.relation.references[7] De Luis D., Fernandez N., Arranz M., Aller R., Izaola O., Romero E. Total homocysteine levels relation with chronic complications of diabetes, body composition, and other cardiovascular risk factors in a population of patients with diabetes mellitus type 2. Journal of Diabetes and its Complications. 19 (1), 42–46 (2005).
dc.relation.references[8] Lei-Da C., Toru S., Frolick M. N. Data mining methods, applications, and tools. Information systems management. 17 (1), 65–70 (2000).
dc.relation.references[9] Koh H. C., Tan G., and others. Data mining applications in healthcare. Journal of healthcare information management. 19 (2), 64–72 (2011).
dc.relation.references[10] Parvez A., Saqib Q., Syed R., Afser Q. Techniques of data mining in healthcare: a review. International Journal of Computer Applications. 120 (15), 38–50 (2015).
dc.relation.references[11] Jothi N., Rashid Nur’Aini Abdul, Husain W. Data mining in healthcare–a review. Procedia computer science. 72, 306–313 (2015).
dc.relation.references[12] Yoo I., Alafaireet P., Marinov M., Pena-Hernandez K., Gopidi R., Chang J.-F., Hua L. Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems. 36 (4), 2431–2448 (2012).
dc.relation.references[13] Tapak L., Mahjub H., Hamidi O., Poorolajal J. Real-data comparison of data mining methods in prediction of diabetes in Iran. Healthcare informatics research. 19 (3), 177–185 (2013).
dc.relation.references[14] Mukesh K., Rajan V., Anshul A. Prediction of Diabetes Using Bayesian Network. International Journal of Computer Science and Information Technologies. 5 (4), 5174–5178 (2014).
dc.relation.references[15] Azrar A., Ali Y., Awaisl M., Zaheer K. Data mining models comparison for diabetes prediction. Int. J. Adv. Comput. Sci. Appl. 9 (8), 320–323 (2018).
dc.relation.references[16] Tramunt B., Smati S., Grandgeorge N., Lenfant F., Arnal J.-F., Montagner A., Gourdy P. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 63 (3), 453–461 (2020).
dc.relation.references[17] Graham J., Cumsille P. E., Shevock A. E. Methods for handling missing data. Handbook of Psychology, Second Edition & Computer Engineering. Vol. 2 (2012).
dc.relation.references[18] Aljuaid T., Sasi S. Proper imputation techniques for missing values in datasets. IEEE: 2016 International Conference on Data Science and Engineering (ICDSE). 1–5 (2016).
dc.relation.references[19] Troyanskaya O., Cantor M., Sherlock G., Brown P., Hastie T., Tibshirani R., Botstein D., Altman R. B. Missing value estimation methods for DNA microarrays. Bioinformatics. 17 (6), 520–525 (2001).
dc.relation.references[20] Abdar M., Kalhori N., Sutikno T., Subroto I. M. I., Arji G. Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases. International Journal of Electrical & Computer Engineering. 5 (6), 1569–1576 (2015).
dc.relation.references[21] Lavanya D., Rani K. U. Performance evaluation of decision tree classifiers on medical datasets. International Journal of Computer Applications. 26 (4), 1–4 (2011).
dc.relation.references[22] Emdin C. A., Anderson S. G., Woodward M., Rahimi K. Usual Blood Pressure and Risk of New-Onset Diabetes: Evidence From 4.1 Million Adults and a Meta-Analysis of Prospective Studies. Journal of the American College of Cardiology. 66 (14), 1552–1562 (2015).
dc.relation.referencesen[1] Definition, diagnosis and classification of diabetes mellitus and its complications: report of a WHO consultation. World Health Organization (1999).
dc.relation.referencesen[2] Meo S. A., Usmani A. M., Qalbani E. Prevalence of type 2 diabetes in the Arab world: impact of GDP and energy consumption. Eur. Rev. Med. Pharmacol. Sci. 21 (6), 1303–1312 (2017).
dc.relation.referencesen[3] Boutayeb W., Lamlili M., Boutayeb A., Derouich M. Mathematical modelling and simulation of b-cell mass, insulin and glucose dynamics: Effect of genetic predisposition to diabetes. Journal of Biomedical Science and Engineering. 7 (6), 330–342 (2014).
dc.relation.referencesen[4] Elhayany A., Lustman A., Abel R., Attal-Singer J., Vinker S. A low carbohydrate Mediterranean diet improves cardiovascular risk factors and diabetes control among overweight patients with type 2 diabetes mellitus: a 1-year prospective randomized intervention study. Diabetes, Obesity and Metabolism. 12 (3), 204–209 (2010).
dc.relation.referencesen[5] Pulgaron E. R., Delamater A. M. Obesity and type 2 diabetes in children: epidemiology and treatment. Current Diabetes Reports. 14 (8), Article number: 508 (2014).
dc.relation.referencesen[6] Donaghue K. C., Chiarelli F., Trotta D., Allgrove J., Dahl-Jorgensen Knut. Microvascular and macrovascular complications. Pediatric Diabetes. 8, 163–170 (2007).
dc.relation.referencesen[7] De Luis D., Fernandez N., Arranz M., Aller R., Izaola O., Romero E. Total homocysteine levels relation with chronic complications of diabetes, body composition, and other cardiovascular risk factors in a population of patients with diabetes mellitus type 2. Journal of Diabetes and its Complications. 19 (1), 42–46 (2005).
dc.relation.referencesen[8] Lei-Da C., Toru S., Frolick M. N. Data mining methods, applications, and tools. Information systems management. 17 (1), 65–70 (2000).
dc.relation.referencesen[9] Koh H. C., Tan G., and others. Data mining applications in healthcare. Journal of healthcare information management. 19 (2), 64–72 (2011).
dc.relation.referencesen[10] Parvez A., Saqib Q., Syed R., Afser Q. Techniques of data mining in healthcare: a review. International Journal of Computer Applications. 120 (15), 38–50 (2015).
dc.relation.referencesen[11] Jothi N., Rashid Nur’Aini Abdul, Husain W. Data mining in healthcare–a review. Procedia computer science. 72, 306–313 (2015).
dc.relation.referencesen[12] Yoo I., Alafaireet P., Marinov M., Pena-Hernandez K., Gopidi R., Chang J.-F., Hua L. Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems. 36 (4), 2431–2448 (2012).
dc.relation.referencesen[13] Tapak L., Mahjub H., Hamidi O., Poorolajal J. Real-data comparison of data mining methods in prediction of diabetes in Iran. Healthcare informatics research. 19 (3), 177–185 (2013).
dc.relation.referencesen[14] Mukesh K., Rajan V., Anshul A. Prediction of Diabetes Using Bayesian Network. International Journal of Computer Science and Information Technologies. 5 (4), 5174–5178 (2014).
dc.relation.referencesen[15] Azrar A., Ali Y., Awaisl M., Zaheer K. Data mining models comparison for diabetes prediction. Int. J. Adv. Comput. Sci. Appl. 9 (8), 320–323 (2018).
dc.relation.referencesen[16] Tramunt B., Smati S., Grandgeorge N., Lenfant F., Arnal J.-F., Montagner A., Gourdy P. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 63 (3), 453–461 (2020).
dc.relation.referencesen[17] Graham J., Cumsille P. E., Shevock A. E. Methods for handling missing data. Handbook of Psychology, Second Edition & Computer Engineering. Vol. 2 (2012).
dc.relation.referencesen[18] Aljuaid T., Sasi S. Proper imputation techniques for missing values in datasets. IEEE: 2016 International Conference on Data Science and Engineering (ICDSE). 1–5 (2016).
dc.relation.referencesen[19] Troyanskaya O., Cantor M., Sherlock G., Brown P., Hastie T., Tibshirani R., Botstein D., Altman R. B. Missing value estimation methods for DNA microarrays. Bioinformatics. 17 (6), 520–525 (2001).
dc.relation.referencesen[20] Abdar M., Kalhori N., Sutikno T., Subroto I. M. I., Arji G. Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases. International Journal of Electrical & Computer Engineering. 5 (6), 1569–1576 (2015).
dc.relation.referencesen[21] Lavanya D., Rani K. U. Performance evaluation of decision tree classifiers on medical datasets. International Journal of Computer Applications. 26 (4), 1–4 (2011).
dc.relation.referencesen[22] Emdin C. A., Anderson S. G., Woodward M., Rahimi K. Usual Blood Pressure and Risk of New-Onset Diabetes: Evidence From 4.1 Million Adults and a Meta-Analysis of Prospective Studies. Journal of the American College of Cardiology. 66 (14), 1552–1562 (2015).
dc.rights.holder© Національний університет “Львівська політехніка”, 2021
dc.subjectбаза даних Gulf COAST
dc.subjectдобування даних
dc.subjectдерево прийняття рішень
dc.subjectаналіз основних компонентів
dc.subjectдіабет 2-го типу
dc.subjectGulf COAST dataset
dc.subjectData Mining
dc.subjectdecision tree
dc.subjectprincipal component analysis
dc.subjectType 2 Diabetes
dc.titleUse of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries
dc.title.alternativeВикористання методу добування даних для прогнозування факторів ризику цукрового діабету другого типу в країнах Перської затоки
dc.typeArticle

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Mathematical Modeling And Computing. – 2021. – Vol. 8, No. 4