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

Table 4_Development of machine learning-based predictive models for fertility intentions in patients with Crohn's disease.xlsx contains data from a study of 276 reproductive-age Crohn's disease patients recruited from a tertiary center in China. The dataset, authored by Jinghan Liu and last updated in May 2026, was used to train and validate interpretable machine learning models predicting fertility intentions based on demographic, clinical, and psychosocial variables.

Use Cases

• Training predictive models for fertility intentions based on demographic and psychosocial variables mentioned in the description.
• Identifying key psychosocial determinants of reproductive decision-making based on SHAP analysis results.
• Benchmarking machine learning algorithms like neural networks and XGBoost for clinical prediction tasks in chronic disease populations.
• Informing the design of structured psychosocial assessments for personalized fertility counseling in IBD care.

Strengths

• Dataset is derived from a study of 276 prospectively recruited patients, providing a specific sample size.
• Model performance metrics are reported, including AUC values of 0.718 and 0.710 for top algorithms.
• Key influential predictors like marital status and perceived family support 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 reflects a single-center study in China, which may limit geographic generalizability.

Provenance

Source

figshare, author Jinghan Liu

Collection Method

Prospective recruitment and standardized assessments of patients from a tertiary center.

Freshness

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

Geography

China (single tertiary center)