JMIR Publications

ABSTRACT

Background:

Preoperative evaluation is important, our study explored the application of machine learning methods for anesthetic risk classification and for the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ–related procedures not involving malignancies.

Objective:

Data on women of reproductive age (age = 20–50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database.

Methods:

We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we employed the log likelihood ratio algorithm to generate comorbidity patterns. Lastly, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction.

Results:

A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score 1–2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score > 2). The area under the receiver operating characteristic curve of the LightGBM model was 90.25.

Conclusions:

By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. Clinical Trial: This study was registered with the Research Ethics Committee of the National Taiwan University Hospital with trial number 202204010RINB