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Polycystic ovary syndrome is the most common endocrine disorder in women of reproductive age, which is still incurable. However, the symptoms can be successfully managed with proper medication and lifestyle interventions. Despite its prevalence, little is known about its etiology. In this review article, the up-to-date diagnostic features and parameters recommended on the grounds of evidence-based data and different guidelines are explored. The ambiguity and insufficiency of data when diagnosing adolescent women have been put under special focus. We look at some of the most recent research done to establish relationships between different gene polymorphisms with polycystic ovary syndrome in various populations along with the underestimated impact of environmental factors like endocrine-disrupting chemicals on the reproductive health of these women. Furthermore, the article concludes with existing treatments options and the scopes for advancement in the near future. Various therapies have been considered as potential treatment through multiple randomized controlled studies, and clinical trials conducted over the years are described in this article. Standard therapies ranging from metformin to newly found alternatives based on vitamin D and gut microbiota could shine some light and guidance toward a permanent cure for this female reproductive health issue in the future. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) has been determined as one of the serious health problems among women which affects women's fertility and leads to crucial health conditions. Hence, early diagnosis of polycystic ovary syndrome can be effective in the treatment process. Recently, machine learning methods have acquired promising results in medical diagnosis. Furthermore, feature selection techniques which generate the most significant subset of features, can reduce the computational time and improve the performance of classifiers. Conventional single machine learning algorithms classify datasets in a single process with an individual model whereas ensemble machine learning algorithms create multiple process with a combination of two or more models which can achieve more accurate results. Therefore, considering the advantages of ensemble classifiers and feature selection methods, in this study, traditional and ensemble classifiers were applied on the Kaggle PCOS dataset to diagnose polycystic ovary syndrome. Furthermore, the performance of various classifiers (i.e., Ensemble Random Forest, Extra Tree, Adaptive Boosting (AdaBoost) and Multi-Layer Perceptron (MLP)) were investigated using the dataset with all features and reduced subsets of features which were generated by filter, embedded and wrapper feature selection methods. The experimental results demonstrated that the feature selection methods had beneficial effects on the improvement of the performance of all classifiers. Moreover, Ensemble Random Forest classifier by using the reduced subset of features based on the embedded feature selection method surpassed other classifiers with Accuracy of 98.89% and Sensitivity of 100% in this study and other studies in the literature. [ABSTRACT FROM AUTHOR]

Objective: Existing data are contradictory on the prevalence of polycystic ovary syndrome (PCOS) and metabolic syndrome (MetS) in women with severe obesity (body mass index [BMI] ≥ 35 kg/m2), and there are few studies investigating the effect of weight reduction in women with severe obesity and PCOS. The aim was to study the prevalence of PCOS and MetS among women with severe obesity and to evaluate the effect of a 12‐months weight loss programme on the prevalence of PCOS and MetS. Design/Participants: In total, 298 women with severe obesity were enrolled whereof 246 women had complete screening data for PCOS and MetS before commencing treatment. Weight loss intervention included very low energy diet. At 12‐months follow‐up, 72 women with complete data remained and were re‐examined with baseline parameters. Results: At baseline, the prevalence of PCOS was 25.6% and in this group, the prevalence of MetS was 43.4% in PCOS vs 43.3% in controls (ns). At 12‐months follow‐up, weight loss in women with PCOS was 12.3 ± 10.7 kg (P < .001) and in non‐PCOS 13.9 ± 13.4 kg (P < .001) with no between group difference. Women without PCOS decreased in total bone mass. Conclusions: Polycystic ovary syndrome occurs in one out of four women with severe obesity. The prevalence of MetS does not differ between women with or without PCOS with severe obesity. There was a significant weight loss in both groups but no difference between groups regarding change in metabolic parameters. [ABSTRACT FROM AUTHOR]