Fertility Intentions in Crohn's Disease Patients with Psychosocial and Clinical Predictors

276 patient records from a prospective study of reproductive-age Crohn's disease patients at a tertiary center in China. The dataset includes demographic, clinical, and psychosocial variables used to train and validate machine learning models predicting fertility intentions. It was created by Jinghan Liu and last updated in May 2026.

Use Cases

• Training binary classification models to predict fertility intention based on demographic and psychosocial features.
• Analyzing the relative influence of factors like marital status and perceived family support on reproductive decision-making.
• Benchmarking model performance (e.g., neural networks vs. XGBoost) on a small, specialized clinical dataset.
• Applying SHAP analysis for model interpretability in a healthcare context.

Strengths

• Includes 276 prospectively recruited patient records with standardized assessments.
• Model performance metrics are reported, including AUC values of 0.718 and 0.710 for top algorithms.
• Key influential predictors, such as marital status and desired number of children, are identified through SHAP analysis.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single center in China, which may limit generalizability to other populations.

Provenance

Source

Prospective study at a tertiary center in China.

Collection Method

Standardized assessments covering demographic, clinical, psychosocial, and fertility-related variables.

Freshness

Last updated 2026-05-22 06:10:55; freshness should be verified.

Geography

China