Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 1 2026 03 01 Hybrid Deep Learning Model for IVF Outcome Prediction from Time-Series Hormonal Data 141 157 106258 10.22059/jitm.2026.106258 EN Shalini B N Research scholar, School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India. Lithin Kumble Associate Professor, School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India. Journal Article 2026 03 06 Optimizing results in assisted reproduction requires tailoring the dosage of follicle-stimulating hormone (FSH) during controlled ovarian stimulation (COS), but this is still challenging because of patient variability. Real-time modifications during stimulation are not supported by the majority of current models, which are restricted to static forecasts of starting dosages. This study proposes an advanced AI-driven framework for forecasting hormone dynamics and improving reproductive outcome prediction in IVF cycles. The methodology integrates multi-source clinical data with high-resolution time-series hormone profiles collected from Day 1 to Day 30 of ovarian stimulation. Data pre-processing includes normalization of hormonal values, alignment of temporal and static clinical attributes, and creation of patient-level merged datasets. Feature engineering incorporates daily hormone variations, moving averages, peak detection, and wavelet-based temporal pattern extraction, alongside encoded and normalized clinical parameters. For hormone trend forecasting, a hybrid deep learning model is developed that combines Wavelet Transform for noise reduction with LSTM and Transformer layers for sequential representation learning. The architecture captures short-term hormone fluctuations and long-range temporal dependencies, enabling accurate next-day hormone prediction. Model performance is optimized using RMSE = 0.31, MAE = 0.22, and MAPE loss metrics. This integrated approach enhances predictive accuracy = 88.9%, facilitates early-cycle monitoring, and supports clinical decision-making in assisted reproductive treatments. https://jitm.ut.ac.ir/article_106258_a3045edf00e55c972c4b03dfe26aa3bb.pdf