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Kokori E, Olatunji G, Aderinto N, Muogbo I, Ogieuhi IJ, Isarinade D, Ukoaka B, Akinmeji A, Ajayi I, Chidiogo E, Samuel O, Nurudeen-Busari H, Muili AO, Olawade DB. The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence. Clin Diabetes Endocrinol 2024; 10:18. [PMID: 38915129 PMCID: PMC11197257 DOI: 10.1186/s40842-024-00176-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 02/20/2024] [Indexed: 06/26/2024] Open
Abstract
Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.
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Affiliation(s)
- Emmanuel Kokori
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Gbolahan Olatunji
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Nicholas Aderinto
- Department of Medicine, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
| | - Ifeanyichukwu Muogbo
- Department of Medicine, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | | | - David Isarinade
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Bonaventure Ukoaka
- Department of Internal Medicine, Asokoro District Hospital, Abuja, Nigeria
| | - Ayodeji Akinmeji
- Department of Medicine and Surgery, Olabisi Onabanjo University, Ogun, Nigeria
| | - Irene Ajayi
- Department of Medicine and Surgery, University of Ilorin, Ilorin, PMB 5000, Nigeria
| | - Ezenwoba Chidiogo
- Department of Medicine and Surgery, AfeBabalola University, Ado-Ekiti, Nigeria
| | - Owolabi Samuel
- Department of Medicine, Lagos State Health Service Commission, Lagos, Nigeria
| | | | | | - David B Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK
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Li YX, Liu YC, Wang M, Huang YL. Prediction of gestational diabetes mellitus at the first trimester: machine-learning algorithms. Arch Gynecol Obstet 2024; 309:2557-2566. [PMID: 37477677 DOI: 10.1007/s00404-023-07131-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/27/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE Short- and long-term complications of gestational diabetes mellitus (GDM) involving pregnancies and offspring warrant the development of an effective individualized risk prediction model to reduce and prevent GDM together with its associated co-morbidities. The aim is to use machine learning (ML) algorithms to study data gathered throughout the first trimester in order to predict GDM. METHODS Two independent cohorts with forty-five features gathered through first trimester were included. We constructed prediction models based on three different algorithms and traditional logistic regression, and deployed additional two ensemble algorithms to identify the importance of individual features. RESULTS 4799 and 2795 pregnancies were included in the Xinhua Hospital Chongming branch (XHCM) and the Shanghai Pudong New Area People's Hospital (SPNPH) cohorts, respectively. Extreme gradient boosting (XGBoost) predicted GDM with moderate performance (the area under the receiver operating curve (AUC) = 0.75) at pregnancy initiation and good-to-excellent performance (AUC = 0.99) at the end of the first trimester in the XHCM cohort. The trained XGBoost showed moderate performance in the SPNPH cohort (AUC = 0.83). The top predictive features for GDM diagnosis were pre-pregnancy BMI and maternal abdominal circumference at pregnancy initiation, and FPG and HbA1c at the end of the first trimester. CONCLUSION Our work demonstrated that ML models based on the data gathered throughout the first trimester achieved moderate performance in the external validation cohort.
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Affiliation(s)
- Yi-Xin Li
- Department of Obstetrics and Gynecology, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China
| | - Yi-Chen Liu
- Department of Nephrology, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China
| | - Mei Wang
- Department of Gynecology, Shanghai Pudong New Area People's Hospital, Shanghai, China
| | - Yu-Li Huang
- Department of Obstetrics and Gynecology, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China.
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Cubillos G, Monckeberg M, Plaza A, Morgan M, Estevez PA, Choolani M, Kemp MW, Illanes SE, Perez CA. Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy. BMC Pregnancy Childbirth 2023; 23:469. [PMID: 37353749 DOI: 10.1186/s12884-023-05766-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/08/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations. METHODS The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance. RESULTS Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72-0.74, accuracy between 0.73-0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82. CONCLUSIONS The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.
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Grants
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
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Affiliation(s)
- Gabriel Cubillos
- Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 8370451, Santiago, Chile
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile
| | - Max Monckeberg
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile
| | - Alejandra Plaza
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile
| | - Maria Morgan
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile
| | - Pablo A Estevez
- Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 8370451, Santiago, Chile
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile
| | - Mahesh Choolani
- Department of Obstetrics and Gynaecology, NUS Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 12, Singapore, 119228, Singapore
| | - Matthew W Kemp
- Department of Obstetrics and Gynaecology, NUS Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 12, Singapore, 119228, Singapore
| | - Sebastian E Illanes
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile.
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile.
| | - Claudio A Perez
- Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 8370451, Santiago, Chile.
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile.
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Tranidou A, Magriplis E, Tsakiridis I, Pazaras N, Apostolopoulou A, Chourdakis M, Dagklis T. Effect of Gestational Weight Gain during the First Half of Pregnancy on the Incidence of GDM, Results from a Pregnant Cohort in Northern Greece. Nutrients 2023; 15:nu15040893. [PMID: 36839252 PMCID: PMC9964795 DOI: 10.3390/nu15040893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
The aim of this study was to evaluate the effect of gestational weight gain (GWG) up to 23+6 weeks of gestation on the incidence of Gestational Diabetes Mellitus (GDM). A pregnant cohort of 5948 women in Northern Greece was recruited. Anthropometric features before and during pregnancy were recorded, the GWG by 23+6 weeks was calculated and a Generalized Linear Regression Model (GLM) with subgroup analyses based on weight status were computed. GDM was diagnosed in 5.5% of women. GLM results showed that GDM likelihood increased with maternal age (MA) and pre-pregnancy BMI (aOR: 1.08, 95%CI: [1.06, 1.11] and aOR: 1.09, 95%CI: [1.09, 1.11], respectively). Ιn the normal pre-pregnancy weight group, when the extra weight gain was >8 kgs, the odds of GDM increased (OR: 2.13, 95%CI: [0.98, 4.21], p = 0.03). Women with pre-pregnancy level 2 clinical obesity (OB2 pre) (BMI > 35 and <40 kg/m2) that shifted to OB3 category (BMI ≥ 40 kg/m2) had an increased GDM likelihood (OR: 4.85, 95%CI: [1.50, 15.95]). Women of higher MA may require stricter monitoring for GDM from early pregnancy, while in obese women, recommended GWG may need to be re-evaluated, since refraining from any weight gain may have a preventive effect for GDM.
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Affiliation(s)
- Antigoni Tranidou
- 3rd Department of Obstetrics and Gynaecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece
| | - Emmanuela Magriplis
- Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece
| | - Ioannis Tsakiridis
- 3rd Department of Obstetrics and Gynaecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece
| | - Nikolaos Pazaras
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
| | - Aikaterini Apostolopoulou
- 3rd Department of Obstetrics and Gynaecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece
| | - Michail Chourdakis
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
| | - Themistoklis Dagklis
- 3rd Department of Obstetrics and Gynaecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece
- Correspondence:
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Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods. INFORMATION 2023. [DOI: 10.3390/info14020092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing values. Additionally, we propose a two-level classification process to reduce the number of false classifications. Our evaluation of the proposed method was conducted using the PIMA Indian dataset for diabetes classification. We compared the performance of five different machine learning models: Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Linear Regression (LR). The results of our experiments show that the SVM classifier achieved the highest accuracy of 94.89%. The RF classifier had the highest precision (98.80%) and the SVM classifier had the highest recall (85.48%). The NB model had the highest F1-Score (95.59%). Our proposed method provides a promising solution for detecting diabetes at an early stage by addressing the issue of missing values in the dataset. Our results show that the use of SVM regression and a two-level classification process can notably improve the performance of machine learning models for diabetes classification. This work provides a valuable contribution to the field of diabetes research and highlights the importance of addressing missing values in machine learning applications.
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Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications. Int J Mol Sci 2022; 23:ijms231810635. [PMID: 36142536 PMCID: PMC9501303 DOI: 10.3390/ijms231810635] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 11/25/2022] Open
Abstract
We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.
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