Aug 2026· Health Science Reports· Vol 9· 0 citations· 33 references
Medicine
TL;DR
Combining hybrid ensemble learning, two-stage feature selection, and XAI approaches provides a computationally efficient, dependable, and interpretable method for PCOS diagnosis and practitioners may find this model to be a useful decision-support tool that improves the accuracy of diagnosis and lessens the need for human interpretation.
Abstract
Polycystic ovary syndrome (PCOS) is the most prevalent endocrine ailment impacting women of reproductive age, distinguished by ongoing imbalances of hormones that lead to the growth of various ovarian cysts alongside other health issues. Infrequent or delayed menstrual periods, and also frequently excessive amounts of androgen, a male hormone, are characteristics of women with PCOS. Despite other consequences, this disease may lead to weight gain, undesirable body hair, type 2 diabetes, and gestational diabetes. The number of cases of PCOS has shockingly ascended in the past few years, according to statistics. But the clinical diagnosis procedure for PCOS in the real world becomes critical since the precision of interpretations greatly relies on the knowledge of the doctor. Therefore, a PCOS prediction model using artificial intelligence might be an appropriate supplement to the lengthy and error‐prone diagnosis method.
The proposed explainable ensemble framework presents a scalable, accurate, and interpretable decision support system for PCOS that is feasible to adopt in practical healthcare environments, especially in rural areas where medical resources are limited and more medical aids for the detection of such diseases are despera...
Aruna Pavate, Tanvi Sawant, S. Vhatkar· Turkish Journal of Engineeri...· 0 citations
Polycystic ovary syndrome (PCOS) is a very common endocrine disorder that affects women of child bearing age across the globe with a global prevalence of between 8-13 percent. Although it is a common disease, PCOS is highly undiagnosed, as the clinical picture is heterogeneous, and the diagnosis is based on costly horm...
Thee pika, S. Megala, My thili et al.· International Journal of Adv...· 0 citations
Findings indicate that explainable machine learning models, particularly KNN and XGBoost, provide accurate and interpretable decision support for early PCOS screening, enabling timely intervention and offering a promising foundation for intelligent healthcare decision-support systems.
Sana Rubab, Musarrat Shaheen, Zohrain Tabassum et al.· Biomedical Informatics and S...· 0 citations
Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women of reproductive age, yet early
risk stratification remains challenging due to its multifactorial nature. This paper proposes an AI-driven
wearable framework for early PCOS prediction built around a novel Neuroendocrine Stress Index (NSI) — a
co...
K. R. Revathy, S. Padmapriya, D. Meenakshi· International Journal of Dru...· 0 citations
SHAP (SHapley Additive exPlanations) analysis supports the assertion that the quantity of follicles, FSH/LH ratio, and the level of LH should be considered the most prevalent predictors, and that the evidence provided by the analysis can be interpreted by clinicians and corresponds to the Rotterdam diagnostic criteria.
Arya Malode, Vijayshri A. Injamuri, Vikul J. Pawar et al.· International journal of com...· 0 citations
Artificial intelligence (AI) should be seen as an aid to earlier case finding, triage, and standardized interpretation rather than as a replacement for clinical judgment in PCOS, because literature has important weaknesses.
Shamsun Nahar, Musammat Shamima Akter, S. Aosaf· Eastern Medical College jour...· 0 citations
At ICSOB 2026 in Gothenburg, we presented TenderIntell, our work on multi-agent support for tendering. This post looks back at the conference, the idea behind a panel of three AI reviewers who score each proposal independently, and why evaluating agents reliably is the question we keep coming back to. The post TenderIntell at ICSOB 2026: Three Days in Gothenburg appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduNov 13, 2026
Founded by Anthony Jules ’92, SM ’92 and Professor Emeritus Rodney Brooks, Robust AI’s robots are designed to collaborate with humans in factories and warehouses.
MIT News · Artificial Intelligence· news.mit.eduNov 13, 2026
