Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods
The paper is devoted to the application of machine learning methods to the prediction of the development of gestational diabetes mellitus in early pregnancy. Based on two publicly available databases, study assesses influence of such features as body mass index, thickness of triceps skin folds, ultr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Igor Sikorsky Kyiv Polytechnic Institute
2021-08-01
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| Series: | Mìkrosistemi, Elektronìka ta Akustika |
| Subjects: | |
| Online Access: | http://elc.kpi.ua/article/view/228845 |
| _version_ | 1811191184075259904 |
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| author | Марко Романович Басараб Екатерина Олеговна Іванько Вішвеш Кулкарні |
| author_facet | Марко Романович Басараб Екатерина Олеговна Іванько Вішвеш Кулкарні |
| author_sort | Марко Романович Басараб |
| collection | DOAJ |
| description | The paper is devoted to the application of machine learning methods to the prediction of the development of gestational diabetes mellitus in early pregnancy. Based on two publicly available databases, study assesses influence of such features as body mass index, thickness of triceps skin folds, ultrasound measurements of maternal visceral fat, first measured fasting glucose, and others a predictors of gestational diabetes mellitus. The supervised machine learning methods based on decision trees, support vector machines, logistic regression, k-nearest neighbors classifier, ensemble learning, Naive Bayes classifier, and neural networks were implemented to determine the best classification models for computerized gestational diabetes mellitus disease prediction. The accuracy of the different classifiers was determined and compared. Support vector machine classifier demonstrated the highest accuracy (83.0% of total correctly prognosed cases, 87.9% for healthy class, and 78.1% for gestational diabetes mellitus) in predicting the development of gestational diabetes based on features from Pima Indians Diabetes Database. Extreme gradient boosting classifier performed the best, comparing to other supervised machine learning methods, for Visceral Adipose Tissue Measurements during Pregnancy Database. It showed 87.9% of total correctly prognosed cases, 82.2% for healthy class, and 93.6% for gestational diabetes mellitus). |
| first_indexed | 2024-04-11T15:02:14Z |
| format | Article |
| id | doaj.art-ee2aff9198ce4e35b706a6632f381f9d |
| institution | Directory Open Access Journal |
| issn | 2523-4447 2523-4455 |
| language | English |
| last_indexed | 2024-04-11T15:02:14Z |
| publishDate | 2021-08-01 |
| publisher | Igor Sikorsky Kyiv Polytechnic Institute |
| record_format | Article |
| series | Mìkrosistemi, Elektronìka ta Akustika |
| spelling | doaj.art-ee2aff9198ce4e35b706a6632f381f9d2022-12-22T04:16:57ZengIgor Sikorsky Kyiv Polytechnic InstituteMìkrosistemi, Elektronìka ta Akustika2523-44472523-44552021-08-0126210.20535/2523-4455.mea.228845Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning MethodsМарко Романович Басараб0Екатерина Олеговна Іванько1Вішвеш Кулкарні2National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»School of Engineering, University of WarwickThe paper is devoted to the application of machine learning methods to the prediction of the development of gestational diabetes mellitus in early pregnancy. Based on two publicly available databases, study assesses influence of such features as body mass index, thickness of triceps skin folds, ultrasound measurements of maternal visceral fat, first measured fasting glucose, and others a predictors of gestational diabetes mellitus. The supervised machine learning methods based on decision trees, support vector machines, logistic regression, k-nearest neighbors classifier, ensemble learning, Naive Bayes classifier, and neural networks were implemented to determine the best classification models for computerized gestational diabetes mellitus disease prediction. The accuracy of the different classifiers was determined and compared. Support vector machine classifier demonstrated the highest accuracy (83.0% of total correctly prognosed cases, 87.9% for healthy class, and 78.1% for gestational diabetes mellitus) in predicting the development of gestational diabetes based on features from Pima Indians Diabetes Database. Extreme gradient boosting classifier performed the best, comparing to other supervised machine learning methods, for Visceral Adipose Tissue Measurements during Pregnancy Database. It showed 87.9% of total correctly prognosed cases, 82.2% for healthy class, and 93.6% for gestational diabetes mellitus).http://elc.kpi.ua/article/view/228845gestational diabetes mellitusdiabetic fetopathymachine learningprediction |
| spellingShingle | Марко Романович Басараб Екатерина Олеговна Іванько Вішвеш Кулкарні Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods Mìkrosistemi, Elektronìka ta Akustika gestational diabetes mellitus diabetic fetopathy machine learning prediction |
| title | Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods |
| title_full | Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods |
| title_fullStr | Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods |
| title_full_unstemmed | Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods |
| title_short | Prediction of the Development of Gestational Diabetes Mellitus in Pregnant Women Using Machine Learning Methods |
| title_sort | prediction of the development of gestational diabetes mellitus in pregnant women using machine learning methods |
| topic | gestational diabetes mellitus diabetic fetopathy machine learning prediction |
| url | http://elc.kpi.ua/article/view/228845 |
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