HYBRID CNN+LSTM+TRANSFORMER ENSEMBLES FOR EARLY PREDICTION OF MULTI-SYSTEM DISEASES IN WOMEN: A CONTEMPORARY REVIEW

Women encounter a wide range of health challenges throughout their lives, including breast cancer, cardiovascular
disease, pregnancy related complications, and malnutrition. These conditions often involve complex, multimodal
data sources such as medical imaging, time series physiological signals, structured electronic health records
(EHR), and patient reported outcomes. Traditional machine learning models frequently struggle to capture the
heterogeneity and temporal dynamics inherent in such data. Hybrid ensemble architectures combining
Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformer models
have emerged as powerful tools for early disease prediction. CNNs extract spatial features from medical images,
LSTMs model sequential dependencies in physiological data, and Transformers handle long range patterns in
structured and unstructured inputs. Ensemble strategies such as stacking, boosting, and bagging enhance
predictive performance by leveraging the strengths of each model type. Comparative analysis across multiple
disease domains demonstrates that hybrid configurations consistently outperform single-model approaches in
terms of accuracy, sensitivity, and robustness. A unified framework, termed All-in-One Women Prediction
(AOWP), integrates multimodal inputs with ensemble learning and interpretability tools such as SHAP and GradCAM to produce clinically actionable insights. Emphasis is placed on fairness, calibration, and external validation
to ensure safe and effective deployment in real world settings. Hybrid deep learning ensembles represent a
promising frontier in women’s health analytics, offering scalable, explainable, and high-performing solutions for
early detection and risk stratification. Future research should prioritize federated learning, lightweight
architectures for low resource environments, and prospective trials to evaluate clinical impact.