Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques
<b>Background/Objectives</b>: Obesity is a major risk factor for diabetes mellitus, a metabolic disease characterized by elevated fasting blood glucose and glycosylated hemoglobin levels. Predicting the percentage and absolute variations in key medical parameters based on weight changes...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/13/5/1116 |
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| author | Oana Vîrgolici Daniela Lixandru Andrada Mihai Diana Simona Ștefan Cristian Guja Horia Vîrgolici Bogdana Virgolici |
| author_facet | Oana Vîrgolici Daniela Lixandru Andrada Mihai Diana Simona Ștefan Cristian Guja Horia Vîrgolici Bogdana Virgolici |
| author_sort | Oana Vîrgolici |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Obesity is a major risk factor for diabetes mellitus, a metabolic disease characterized by elevated fasting blood glucose and glycosylated hemoglobin levels. Predicting the percentage and absolute variations in key medical parameters based on weight changes can help patients stay motivated to lose weight and assist doctors in making informed lifestyle and treatment recommendations. This study aims to assess the extent to which weight variation influences the absolute and percentage changes in various clinical parameters. <b>Methods</b>: The dataset includes medical records from patients in Bucharest hospitals, collected between 2012 and 2016. Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. Model performance was evaluated using Mean Squared Error, Mean Absolute Error, and R<sup>2</sup> score. <b>Results</b>: Almost all models demonstrated promising predictive performance. Quantitative predictions were made for each parameter, highlighting the relationship between weight loss and improvements in clinical indicators. <b>Conclusions</b>: Weight loss led to significant improvements in dysglycemia, dyslipidemia, inflammation, uric acid levels, liver enzymes, thyroid hormones, and blood pressure, with reductions ranging from 5% to 30%, depending on the parameter. |
| format | Article |
| id | doaj-art-e38ea7cd3244487a808085818a7d0610 |
| institution | OA Journals |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj-art-e38ea7cd3244487a808085818a7d06102025-08-20T02:33:30ZengMDPI AGBiomedicines2227-90592025-05-01135111610.3390/biomedicines13051116Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning TechniquesOana Vîrgolici0Daniela Lixandru1Andrada Mihai2Diana Simona Ștefan3Cristian Guja4Horia Vîrgolici5Bogdana Virgolici6Academy of Economic Studies, 010374 Bucharest, RomaniaFaculty of Midwifery and Nursing, ”Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, RomaniaFaculty of Medicine, ”Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, RomaniaNational Institute of Diabetes, Nutrition and Metabolic Disease “N. Paulescu”, 020475 Bucharest, RomaniaFaculty of Medicine, ”Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, RomaniaFaculty of Medicine, ”Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, RomaniaFaculty of Medicine, ”Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania<b>Background/Objectives</b>: Obesity is a major risk factor for diabetes mellitus, a metabolic disease characterized by elevated fasting blood glucose and glycosylated hemoglobin levels. Predicting the percentage and absolute variations in key medical parameters based on weight changes can help patients stay motivated to lose weight and assist doctors in making informed lifestyle and treatment recommendations. This study aims to assess the extent to which weight variation influences the absolute and percentage changes in various clinical parameters. <b>Methods</b>: The dataset includes medical records from patients in Bucharest hospitals, collected between 2012 and 2016. Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. Model performance was evaluated using Mean Squared Error, Mean Absolute Error, and R<sup>2</sup> score. <b>Results</b>: Almost all models demonstrated promising predictive performance. Quantitative predictions were made for each parameter, highlighting the relationship between weight loss and improvements in clinical indicators. <b>Conclusions</b>: Weight loss led to significant improvements in dysglycemia, dyslipidemia, inflammation, uric acid levels, liver enzymes, thyroid hormones, and blood pressure, with reductions ranging from 5% to 30%, depending on the parameter.https://www.mdpi.com/2227-9059/13/5/1116machine learningbody mass indexdiabetes mellitusdiet and nutritionmetabolic healthweight loss |
| spellingShingle | Oana Vîrgolici Daniela Lixandru Andrada Mihai Diana Simona Ștefan Cristian Guja Horia Vîrgolici Bogdana Virgolici Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques Biomedicines machine learning body mass index diabetes mellitus diet and nutrition metabolic health weight loss |
| title | Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques |
| title_full | Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques |
| title_fullStr | Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques |
| title_full_unstemmed | Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques |
| title_short | Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques |
| title_sort | prediction of metabolic parameters of diabetic patients depending on body weight variation using machine learning techniques |
| topic | machine learning body mass index diabetes mellitus diet and nutrition metabolic health weight loss |
| url | https://www.mdpi.com/2227-9059/13/5/1116 |
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