Uterine disorders classification based on symptomic and behaviorial data

The Uterine Disorder Diagnostic Dataset (UDD-2025) consists of 223 anonymized patient records collected through hand-on interviews and clinical ultrasound reports from the Department of Gynecology, R.S. Unit, Shaheed Tajuddin Ahmad Medical College Hospital, Bangladesh. The data collection took place between May 5 – August 7, 2025, spanning a total of 95 days.

Each participant record includes validated diagnostic parameters extracted directly from ultrasound and medical reports, along with corresponding symptomatic and behavioral indicators gathered via structured in-person interviews. Each participant gave consent to interview willingly and shared their experience without any external influence. The dataset is stored in a single .xlsx file (which can be converted to a csv file), with each row representing one participant and each column corresponding to a clinical or behavioral variable.

The ethical clearance for this dataset was obtained from the Institutional Ethics Committee of Daffodil International University, and official permission for clinical data collection was granted by the Assistant Director of Shaheed Tajuddin Ahmad Medical College Hospital.

All diagnostic fields were verified and approved by experts at the same department before inclusion. The ultrasound-based diagnostic summaries were then manually entered and validated for consistency by the research team. All diagnosis summaries were input manually from the reports, maintaining the sequence of other data of each participant. No personally identifiable information is present in the final .xlsx file.

The ultrasound report summaries for each patient were collected immediately following their consultation with the attending physician. As approval had been obtained from the relevant institutional authorities, the procedure ensured the validity and reliability of the diagnostic information.

This dataset serves as a reliable resource for research in:
-Predictive modeling of uterine disorders
-Statistical correlation between menstrual behavior and clinical diagnosis
-Machine learning applications in reproductive health diagnostics
-Multimodal health data integration and analysis

By combining expert-validated ultrasound diagnostics with real-world behavioral data, the UDD-2025 dataset provides an authentic foundation for advancing AI-based women’s health research, reproductive disease prediction, and evidence-driven gynecological analytics.