# Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate

## TL;DR

This study creates a machine learning model that predicts postoperative complications in gastric cancer patients using body fat rate and other clinical data, improving on traditional BMI-based methods.

## Contribution

A novel AutoML framework integrating body composition indices and clinicopathological features for predicting postoperative complications in gastric cancer patients.

## Key findings

- The model achieved high performance with ROC-AUC of 0.9380 and PR-AUC of 0.9262 in independent testing.
- Key predictors included body fat rate, visceral fat density, and C-reactive protein, among others.
- The model showed strong generalizability and clinical utility through decision curve analysis and calibration curves.

## Abstract

To develop an automated machine learning (AutoML) framework integrating body composition indices—notably Body Fat Rate (BFR)—and clinicopathological features for predicting postoperative complications in gastric cancer patients, addressing limitations of traditional body mass index (BMI) assessment and enhancing clinical translatability.

In this retrospective cohort study, 1,023 gastric cancer patients undergoing radical gastrectomy (January 2020–January 2025) were enrolled across two hospitals (716 training, 307 testing). A dual-optimization workflow included: (1) Simultaneous feature selection and hyperparameter tuning via the Improved Hike Optimization Algorithm (IHOA); (2) Class imbalance mitigation using synthetic minority oversampling technique (SMOTE). Model performance was evaluated through accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), calibration curves, and decision curve analysis (DCA). Feature robustness was validated using least absolute shrinkage and selection operator regression, while SHapley Additive exPlanations (SHAP) interpreted predictor contributions. A MATLAB-based proof-of-concept prototype visualization tool was developed for implementation.

In independent testing, AutoML maintained robust performance (ROC-AUC = 0.9380, PR-AUC = 0.9262). DCA revealed greater net clinical benefit across risk thresholds (1%–93%) compared to conventional methods, with sustained high-level stability confirming superior generalizability. Calibration curves demonstrated optimal probabilistic prediction (lowest test-set Brier score = 0.111). SHAP analysis identified BFR, visceral fat density (VFD), visceral fat area (VFA), skeletal muscle area (SMA), C-reactive protein (CRP), BMI, Age and lymphadenectomy extent as key predictors.

The AutoML prediction model developed in this study achieves both high precision and strong interpretability. Its visualized tool effectively overcomes barriers to clinical translation, providing intelligent decision support for early warning and personalized intervention of postoperative complications in gastric cancer.