ArticleJournal of endocrinological investigation2022
Polycystic ovary syndrome: clinical and laboratory variables related to new phenotypes using machine-learning models.
Article in Journal of endocrinological investigation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.Frontiers in endocrinology · 2023Pooled it
- A Two-Stage Hybrid Feature Selection and Ensemble Learning Framework With Explainable AI for Accurate PCOS Prediction.Health science reports · 2026Article
- AI-quantum framework for accurate infertility risk classification in PCOS patients using EHR data.Frontiers in artificial intelligence · 2026Article
- Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.Scientific reports · 2025Article
- Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China.Scientific reports · 2025Article
- Polycystic ovary syndrome as a metabolic disease.Nature reviews. Endocrinology · 2025Review
- Development of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS.Journal of ovarian research · 2025Article
- Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.PloS one · 2025Article
- Oviduct Glycoprotein 1 (OVGP1) Diagnoses Polycystic Ovary Syndrome (PCOS) Based on Machine Learning Algorithms.ACS omega · 2024Article
- Predicting TCM patterns in PCOS patients: An exploration of feature selection methods and multi-label machine learning models.Heliyon · 2024Article
- Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media.PloS one · 2024Article
- Polycystic Ovary Syndrome: Pathophysiology and Controversies in Diagnosis.Diagnostics (Basel, Switzerland) · 2023Review
- Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.Quantitative imaging in medicine and surgery · 2022Article
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePolycystic Ovary Syndrome (PCOS) is the most frequent endocrinopathy in women of reproductive age. Machine learning (ML) is the area of artificial intelligence with a focus on predictive computing algorithms. We aimed to define the most relevant clinical and laboratory variables related to PCOS diagnosis, and to stratify patients into different phenotypic groups (clusters) using ML algorithms.
methodsVariables from a database comparing 72 patients with PCOS and 73 healthy women were included. The BorutaShap method, followed by the Random Forest algorithm, was applied to prediction and clustering of PCOS.
resultsAmong the 58 variables investigated, the algorithm selected in decreasing order of importance: lipid accumulation product (LAP); abdominal circumference; thrombin activatable fibrinolysis inhibitor (TAFI) levels; body mass index (BMI); C-reactive protein (CRP), high-density lipoprotein cholesterol (HDL-c), follicle-stimulating hormone (FSH) and insulin levels; HOMA-IR value; age; prolactin, 17-OH progesterone and triglycerides levels; and family history of diabetes mellitus in first-degree relative as the variables associated to PCOS diagnosis. The combined use of these variables by the algorithm showed an accuracy of 86% and area under the ROC curve of 97%. Next, PCOS patients were gathered into two clusters in the first, the patients had higher BMI, abdominal circumference, LAP and HOMA-IR index, as well as CRP and insulin levels compared to the other cluster.
conclusionThe developed algorithm could be applied to select more important clinical and biochemical variables related to PCOS and to classify into phenotypically different clusters. These results could guide more personalized and effective approaches to the treatment of PCOS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.