Machine Learning Techniques to Predict Readmissions After Bariatric Surgery

Monday, 22 July 2024, 23:30

This study explores the development of a machine learning model to predict 30-day readmissions following bariatric surgery by analyzing laboratory test data. Data from 1262 patients were examined, revealing a 7.69% readmission rate. The study employs various models, with the support vector machine (SVM) achieving the best performance in predicting at-risk patients. The findings highlight the potential of machine learning to enhance patient monitoring and reduce readmission rates.
Nature
Machine Learning Techniques to Predict Readmissions After Bariatric Surgery

Introduction

The purpose of the study was to develop a machine learning model for predicting 30-day readmission after bariatric surgery based on laboratory tests.

Data Collection

Data were collected from patients who underwent *bariatric surgery* between 2018 and 2023. Laboratory test indicators from the preoperative stage, one day postoperatively, and three days postoperatively were analyzed.

Model Selection

Least absolute shrinkage and selection operator regression was used to select the most relevant features. Models constructed in the study included:

  • Support Vector Machine (SVM)
  • Generalized Linear Model
  • Multi-layer Perceptron
  • Random Forest
  • Extreme Gradient Boosting

Model Performance

Model performance was evaluated and compared using the area under the receiver operating characteristic curve (AUROC). A total of 1262 patients were included, with 7.69% of cases being readmitted.

Results

The SVM model achieved the highest AUROC value of 0.784 (95% CI 0.696–0.872), outperforming other models. This suggests that machine learning models based on laboratory test data can effectively identify patients at high risk of readmission after bariatric surgery.


This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.


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