Clinical Data for Predicting Relapse in Idiopathic Nephrotic Syndrome, 2022-2024

562 adult patient records from a retrospective cohort study conducted between January 2022 and January 2024. The dataset, created by Mingrui Zhao, was used to develop and compare logistic regression, random forest, and deep learning models for predicting relapse within 12 months.

Use Cases

• Train a relapse prediction model based on baseline clinical and laboratory data mentioned in the description
• Compare the performance of logistic regression, random forest, and deep learning models for a binary medical outcome
• Identify influential predictors of relapse based on features like steroid resistance and proteinuria
• Apply SMOTE techniques to address class imbalance in a clinical dataset

Strengths

• Dataset contains 562 patient records with a defined 12-month outcome.
• Model performance metrics (AUC, accuracy, sensitivity) are reported for three different algorithms.
• Key predictors like steroid resistance and proteinuria are identified, providing interpretable insights.

Limitations

• Row count for the actual data table is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single retrospective study; external validation is noted as needed.

Provenance

Source

Mingrui Zhao via figshare

Collection Method

Retrospective cohort study of patients treated between January 2022 and January 2024.

Time Range

January 2022 to January 2024

Freshness

Last updated 2026-04-13 04:30:45; freshness should be verified.

Geography

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