Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning
Diabetes during pregnancy is a major source of health problems in unborn infants and their moms. Because gestational diabetes can develop to permanent diabetes, ML is an important method for predicting the likelihood of such progression based on the given features. Although the current study may pre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01047.pdf |
| _version_ | 1797658800449126400 |
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| author | Shamila M. Srinivas M. Aryan Suresh H. Viswa Sai Poojith T. Kumar Dharmendra Harikrishna Bommala |
| author_facet | Shamila M. Srinivas M. Aryan Suresh H. Viswa Sai Poojith T. Kumar Dharmendra Harikrishna Bommala |
| author_sort | Shamila M. |
| collection | DOAJ |
| description | Diabetes during pregnancy is a major source of health problems in unborn infants and their moms. Because gestational diabetes can develop to permanent diabetes, ML is an important method for predicting the likelihood of such progression based on the given features. Although the current study may predict lifelong diabetes in pregnant women, it cannot predict the likelihood of neonatal diabetes. As a result, new characteristics are required to improve the forecasting of neonatal mellitus and provide the most accurate and feasible diabetes persistence results in pregnant women. Python scripting and the application of Machine Learning methods such as SVM, KNN, and LR can assist in achieving this aim. The preprocessing ML dataset focusing on Diabetes from the Pima Indian diabetes database collected through Kaggle. In addition, two new attributes were added to the paper’s dataset. According to research, machine learning models using characteristics like SVM and decision trees may successfully predict the risk of diabetes in pregnant women. Various factors have been used to predict the beginning of this condition during pregnancy. |
| first_indexed | 2024-03-11T18:04:28Z |
| format | Article |
| id | doaj.art-c691a8a22e544b61bdd13f3235337d2b |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-11T18:04:28Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c691a8a22e544b61bdd13f3235337d2b2023-10-17T08:47:10ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014300104710.1051/e3sconf/202343001047e3sconf_icmpc2023_01047Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine LearningShamila M.0Srinivas M.1Aryan Suresh H.2Viswa Sai Poojith T.3Kumar Dharmendra4Harikrishna Bommala5Department of CSE (AI & ML), GRIETDepartment of CSE (AI & ML), GRIETDepartment of CSE (AI & ML), GRIETDepartment of CSE (AI & ML), GRIETUttaranchal Institute of Technology, Uttaranchal UniversityKG Reddy College of Engineering & TechnologyDiabetes during pregnancy is a major source of health problems in unborn infants and their moms. Because gestational diabetes can develop to permanent diabetes, ML is an important method for predicting the likelihood of such progression based on the given features. Although the current study may predict lifelong diabetes in pregnant women, it cannot predict the likelihood of neonatal diabetes. As a result, new characteristics are required to improve the forecasting of neonatal mellitus and provide the most accurate and feasible diabetes persistence results in pregnant women. Python scripting and the application of Machine Learning methods such as SVM, KNN, and LR can assist in achieving this aim. The preprocessing ML dataset focusing on Diabetes from the Pima Indian diabetes database collected through Kaggle. In addition, two new attributes were added to the paper’s dataset. According to research, machine learning models using characteristics like SVM and decision trees may successfully predict the risk of diabetes in pregnant women. Various factors have been used to predict the beginning of this condition during pregnancy.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01047.pdf |
| spellingShingle | Shamila M. Srinivas M. Aryan Suresh H. Viswa Sai Poojith T. Kumar Dharmendra Harikrishna Bommala Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning E3S Web of Conferences |
| title | Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning |
| title_full | Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning |
| title_fullStr | Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning |
| title_full_unstemmed | Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning |
| title_short | Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning |
| title_sort | feasible prediction of diabetes in pregnant woman and neonatal mellitus in new born child using machine learning |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01047.pdf |
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