We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period. We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model. 97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively. The XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries.

Objective: This study was aimed to identify the most effective machine learning (ML) algorithm for predicting preeclampsia based on sociodemographic and obstetric factors during the preconception period. Methods: Data from pregnant women admitted to the obstetric clinic during their first trimester were analyzed, focusing on maternal age, body mass index (BMI), smoking status, history of diabetes mellitus, gestational diabetes mellitus, and mean arterial pressure. The women were grouped by whether they had a preeclampsia diagnosis and by whether they had one or two live births. Predictive models were then developed using five commonly applied ML algorithms. Results: The study included 100 mothers divided into four groups: 22 nulliparous mothers with preeclampsia, 25 nulliparous mothers without preeclampsia, 28 parous mothers with preeclampsia, and 25 parous mothers without preeclampsia. Analysis showed that maternal BMI and family history of diabetes mellitus were the most significant predictive variables. Among the predictive models, the extreme gradient boosting (XGB) classifier demonstrated the highest accuracy, achieving 70% and 72.7% in the respective groups. Conclusions: A predictive model utilizing an ML algorithm based on maternal sociodemographic data and obstetric history could serve as an early detection tool for preeclampsia.

Aim: To investigate the effect of COVID-19 on sexual dysfunction in women. Materials and Methods: The women diagnosed with COVID-19 and hospitalized at a tertiary hospital were included. They completed the Introductory Data Form, the Female Sexual Function Index-(FSFI), and the Short Form-36 Quality of Life Scale (SF-36). Results: 15 women between the ages of 19-49 who completed the treatment protocol, discharged at least 14 days before, and who had not been diagnosed as sexual dysfunction sexually active earlier were included to the study. It was detected that weekly sexual intercourse before and after COVID-19 significantly differed (p=0.047). The frequency of relationships decreased statistically after COVID-19. The mean value of the FSFI satisfaction score differs from COVID-19 before and after diagnosis (p=0.012). The mean satisfaction score before COVID-19 was 3.47, and after COVID-19 was 2.93. The score of the subgroups of FSFI did not differ from COVID-19 before and after diagnosis (p>0.050). The median value of SF-36 pain differs from COVID-19 before and after diagnosis (p=0.008). Conclusion: it was concluded that the frequency of sexual intercourse, sexual satisfaction in women decreased after COVID-19 disease, and the quality of life scores did not change in a statistically significant way.

This study aimed to predict the likelihood of natural conception among couples by using a machine learning (ML) approach based on sociodemographic and sexual health data. This marks a novel, non-invasive methodology for fertility prediction. This prospective study included 197 couples divided into two groups: 98 fertile couples (Group 1) who achieved natural conception within one year, and 99 infertile couples (Group 2) who were unable to conceive despite regular unprotected intercourse. Data were collected using a structured form capturing 63 variables from both partners. Using the Permutation Feature Importance method, 25 key predictors were selected. The variables included BMI, age, menstrual cycle characteristics, and varicocele presence. Five ML models were developed and their performance was evaluated using metrics such as accuracy, sensitivity, and specificity. The XGB Classifier showed the highest performance among the models tested with an accuracy of 62.5% and a ROC-AUC of 0.580, indicating limited predictive capacity. The selected predictors encompassed a balance of medical, lifestyle, and reproductive factors for both partners, emphasizing the couple-based approach. Key factors included BMI, caffeine consumption, history of endometriosis, and exposure to chemical agents or heat. This study assessed the use of ML to predict natural conception using sociodemographic and health data The key predictors identified emphasize the importance of couple-based and lifestyle factors in predicting natural conception. However, the predictive capacity of the models was limited, highlighting the need for future studies with larger datasets and expanded predictors to improve accuracy and facilitate AI integration into fertility assessment.

Objective: This study was conducted to determine the effect of hyperemesis gravidarum diagnosis in pregnant women on their acceptance of pregnancy and quality of life. Methods: This case-control study was conducted with 375 pregnancy 175 diagnosed with hyperemesis gravidarum [case group], 200 pregnant women with healthy pregnancy [control group], between July 2018 and February 2019, in Turkey. Data were collected with the Personal Information Form, World Health Organization’s Quality of Life Questionnaire Brief Version was used to ascertain quality of life and to determine adaptation to pregnancy, the “Prenatal Self-Evaluation Scale” was applied. Only the “Acceptance of Pregnancy” subdimensions were included in the study. Results: Pregnancy acceptance was found to be significantly lower in pregnant women with hyperemesis gravidarum than in healthy pregnant women. The quality of life levels of the pregnant women with hyperemesis gravidarum were found to be lower than those in the healthy pregnant women. Conclusion: Quality of life is affected by hyperemesis gravidarum. In addition, low quality of life and hyperemesis gravidarum negatively affect the acceptance of pregnancy. This result highlights the fact that healthcare professionals should develop a strategy to improve the quality of life of women with hyperemesis and help them to accept their pregnancy.