Leveraging Integrated Data Warehousing and Interactive Dashboards to Monitor Success Patterns in Assisted Reproduction

Precise tracking of In-Vitro Fertilization (IVF) procedures is vital for improving medical results and refining patient guidance. Nevertheless, the intricate nature of multidimensional clinical datasets, which include diverse patient profiles and medical protocols, frequently prevents fertility centers from obtaining a comprehensive understanding of success trends. This study introduces "Fertility Insights," an analytical approach utilizing the CRISP-ML(Q) methodology to simplify IVF oversight through data-centered visualization. The architecture employs a local data stream where primary clinical files are refined via Python for stringent data validation and normalization. The cleaned data is then hosted in a unified MySQL repository, maintaining data consistency and organized retrieval for longitudinal studies. Lastly, dynamic dashboards are created within Power BI to illustrate essential performance metrics, including the variance in success rates. This system equips healthcare professionals with practical intelligence regarding therapeutic impact and facility productivity. This research highlights how descriptive modeling can effectively turn complicated reproductive data into a functional resource for clinical strategy.