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Hybrid Deep Learning Model for IVF Outcome Prediction from Time-Series Hormonal Data | ||
| Journal of Information Technology Management | ||
| دوره 18، شماره 1، 2026، صفحه 141-157 اصل مقاله (1.04 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2026.106258 | ||
| نویسندگان | ||
| Shalini B N* 1؛ Lithin Kumble2 | ||
| 1Research scholar, School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India. | ||
| 2Associate Professor, School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Follicle-stimulating hormones؛ IVF cycle؛ Wavelet transform؛ LSTM with transfer layer؛ RMSE؛ MAPE score | ||
| مراجع | ||
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