Aug 2026· Biomedical Informatics and Smart Healthcare· 0 citations· 32 references
TL;DR
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.
Abstract
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting reproductive-age women, leading to infertility, hormonal imbalance, insulin resistance, and cardiovascular complications. Early diagnosis remains challenging due to heterogeneous manifestations, overlapping symptoms, and lack of automated screening tools. To address these issues, this study presents a comprehensive comparative framework for PCOS prediction using machine learning and deep learning on a public dataset of 541 patient records. The framework incorporates missing value imputation, feature standardization, SMOTE class balancing, and correlation-based feature selection. Five machine learning algorithms (Decision Tree, KNN, SVM, Random Forest, XGBoost) and two deep learning architectures (LSTM, CNN-ResNet) were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC, with confusion matrices employed for detailed classification analysis. KNN achieved the highest accuracy (92.66\%), recall (91.67\%), and F1-score (89.19\%), while XGBoost delivered the best discriminative capability (ROC-AUC = 0.9540). Among deep learning models, LSTM consistently outperformed CNN-ResNet, demonstrating superior ability to capture complex clinical feature relationships. SHAP-based explainability identified ovarian follicle counts, menstrual irregularities, hair growth, weight gain, skin darkening, and anti-Müllerian hormone (AMH) levels as the most influential predictors. These 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.
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.
Md Rakibul Hasan Efty, M. Rohman, K. M. Uddin et al.· Health Science Reports· 0 citations
Polycystic Ovarian Syndrome (PCOS) remains a prevalent endocrine challenge for women in their reproductive years. Because its clinical manifestations are so diverse, achieving an accurate diagnosis is frequently difficult, necessitating a comprehensive and detailed medical evaluation. A late diagnosis of PCOS can result in severe long-term health issues, such as infertility, hormonal imbalance, and cardiovascular disease. To address existing diagnostic gaps, this research introduces a multifaceted hybrid framework. It synergizes machine learning algorithms to process patient-reported questionnaire data with deep learning architectures specifically designed for the automated interpretation of ovarian ultrasound imagery. XGBoost is used to analyze structured questionnaire data to assess symptom patterns, lifestyle variables, and clinical variables related to PCOS. At the same time, DenseNet-121 is employed to classify ovarian ultrasound images by learning robust visual representations. For better interpretation of the model's decisions, Grad-CAM is used to point out the areas in the ultrasound images that impact the prediction results. Experimental findings show that the proposed method provides reliable classification accuracy with transparency in the decision-making process.
A. Agnal, Jasna Nevis A, Janani Sriraman et al.· International Conference on...· 0 citations
Polycystic Ovary Syndrome (PCOS) is a disease that has spread across the globe and has become a significant health concern that mainly affects women of reproductive age. The detection, diagnosis, treatment, and management of the condition at an early stage are vital in order to lower the risk of long-term complications, primarily an elevated risk of type 2 diabetes and gestational diabetes. In line with advancements in computational methods, machine learning and ensemble learning techniques have drawn significant attention as a means of facilitating automated medical diagnosis. This paper aims to build a reliable, efficient, and interpretable PCOS diagnostic system that not only supports evidence-based decision-making but also provides global explanations of feature contributions. To identify the best-performing model and reduce the number of features, six machine learning algorithms-Logistic Regression, Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, and k-Nearest Neighbors-have been employed, with Bayesian hyperparameter tuning applied for optimization. High-performing base learners, together with a meta-learner, were then stacked using an ensemble approach to further enhance predictive performance. Experiments were conducted on a publicly available PCOS dataset using two train-test split ratios (70:30 and 80:20). The results show that the stacking ensemble model along with optimized features demonstrated improved performance over individual baseline models.
Pooja Snehal Janwe, Nazia Nusrath Ul Ain, K. Radhika et al.· International Conference on...· 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
Polycystic ovary syndrome (PCOS) is one of the commonest endocrine disorders in women of reproductive age, yet diagnosis is often delayed because symptoms are varied and routine care may fail to integrate menstrual history, androgen excess, laboratory findings, and ovarian imaging. Earlier recognition matters because PCOS affects fertility, metabolic health, pregnancy, psychological well-being, and quality of life. AI has therefore attracted growing interest in prediction and early detection. Relevant tools include machine-learning models for structured clinical data, deep-learning systems for ultrasound images, natural-language processing of free-text reports, and digital platforms that generate risk scores from symptoms or menstrual-tracking data. Published studies suggest promise in symptom-based triage, EHR case finding, ultrasound standardization, and emerging omics or mobile-health approaches. At the same time, literature has important weaknesses. Many studies are retrospective, single-center, or based on small, curated datasets. Diagnostic definitions are not always consistent, external validation is limited, and only a minority of studies explain how predictions would be used in real clinical practice. There are also age-specific concerns: adult criteria cannot simply be transferred to adolescents, because current guidance does not support the use of ovarian morphology or anti-mullerian hormone (AMH) as diagnostic criteria in adolescents. At present, AI should be seen as an aid to earlier case finding, triage, and standardized interpretation rather than as a replacement for clinical judgment. The most useful future systems will be the ones that are clinically understandable, fair across patient groups, validated in diverse settings, and tested in routine care pathways.
Eastern Med Coll J. January 2026 Vol.11 No.1 : 82-92
Shamsun Nahar, Musammat Shamima Akter, S. Aosaf· Eastern Medical College jour...· 0 citations
Polycystic Ovary Syndrome (PCOS) is a common complex hormonal condition that disrupts the balance in metabolism, fertility, and dermatological health, especially among women of reproductive age. It's mixed, and superimposing clinical analysis often tends to slow down the correct diagnosis. However, in the recent past, the convergence of data science and healthcare has made it possible to analyse large-scale patient data to gain a deeper understanding of complex diseases such as PCOS. The suggested methodology implies the use of a multi-dimensional database comprising physiological measurements, hormone concentration, lifestyle factors, and clinical indicators to find trends that can be related to the condition. A data-driven systematic method is used to examine statistical relations and hidden patterns. Pre-processing of data (dealing with missing values, outlier detection, normalising values and categorical encoding) is done, followed by the Exploratory Data Analysis (EDA), which reveals the major associations and visual representations of the distributions of variables. The feature engineering adds new features like hormone ratios, BMI groups and waist to hip measurements to enhance model performance. Improved oversampling methods, namely ADASYN and Borderline-SMOTE, reduce the imbalance between classes, and state-of-the-art models of machine learning, such as Logistic Regression, Random Forest, XGBoost, CatBoost, and ensemble voting classifiers, are applied to the PCOS classification. Clinical relevance is statistically tested (Chi-square, t-tests, ANOVA), and the explainable AI using SHAP is used to complement model interpretability. The findings aid in describing the significance of the machine learning methodology to be supplemented by the analytical methods to produce useful clinical data, enhance the quality of diagnosis, and promote the initial PCOS detection, thus contributing to the general achievement of AI in the field of prophylaxis and personalised medicine.
Abhinav Pathak, M. Sujithra, H. P. et al.· Pertanika journal of science...· 0 citations
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