NCT06399081

Construction of a Predictive Model of Gangrenous Cholecystitis Based on Machine Learning

A Real-world Study of Predictive Models of Gangrenous Cholecystitis Based on Machine Learning

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,006 (actual)
• Sponsor: Dalian Medical University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

Gangrenous cholecystitis is the most common complication of acute cholecystitis. There is no research using machine learning models to construct predictive diagnostic models for gangrenous cholecystitis.

Detailed description

This study reviewed the clinical data of 2023 cholecystectomy patients admitted to our center between January 1, 2015, and May 31, 2015, it includes demographic, clinical features, laboratory and imaging indexes, and constructs five commonly used Decision Tree, SVM, Random Forest, XGBoost, AdaBoost models, feature subsets are selected by Recursive Feature Elimination with Cross-Validation and the importance of variables in each model, model performance is evaluated by Balanced accuracy, Recall, Precision, F1score, and the Precision-Recall(PR) curve, and the final results are verified by independent external validation sets.

Conditions

• Gangrenous Cholecystitis

Interventions

Type	Name	Description
OTHER	Observational	Observational

Timeline

• Start date: 2023-12-01
• Primary completion: 2024-03-01
• Completion: 2024-03-02
• First posted: 2024-05-03
• Last updated: 2024-05-03

Locations

1 site across 1 country: China