Hybrid Deep Learning Model for IVF Outcome Prediction from Time-Series Hormonal Data

Document Type : Research Paper

Authors

1 Research scholar, School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India.

2 Associate Professor, School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India.

10.22059/jitm.2026.106258

Abstract

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.

Keywords

References

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Journal of Information Technology Management
Volume 18, Issue 1
2026
Pages 141-157

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