A data-driven approach to PCOS Diagnosis: Systematic review of machine learning applications in reproductive health
Bibliographic record
Abstract
Abstract Background and aim : Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder in reproductive-aged women, characterized by hormonal imbalances, anovulation, and metabolic abnormalities. This systematic review aims to evaluate the effectiveness, types, and diagnostic performance of ML algorithms applied in PCOS detection and classification, and to identify the most frequently used input features and methodological challenges in existing studies. Methods : A systematic search was conducted across scholarly databased, but not limited to PubMed, Scopus, and Google Scholar for studies published between 2014 and 2024 using keywords related to PCOS and machine learning. Inclusion criteria focused on original, peer-reviewed studies applying ML models for PCOS diagnosis. Data were extracted on model type, input features, diagnostic accuracy, and study design. Quality assessment was performed using the PROBAST tool. Results : Out of 450 identified studies, 34 met the inclusion criteria and passed the quality assessment. Supervised learning models such as Random Forest, SVM, and XGBoost showed high accuracy (up to 99%). Deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieved accuracies between 95% and 99.89% in analyzing ultrasound images. Hybrid models integrating clinical and imaging data further enhanced performance. Common input features included BMI, LH/FSH ratio, AMH, and ultrasound-based ovarian morphology. However, few studies validated models on external datasets, and input feature selection lacked standardization. Conclusion : Machine learning models such as supervised, deep learning, and hybrid approaches show strong potential in improving PCOS diagnosis by identifying complex patterns across multi-dimensional datasets. Challenges such as limited generalizability and data standardization remain, therefore future studies should focus on developing explainable ML tools, validating models in clinical settings, and leveraging diverse data types for robust, personalized PCOS diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".