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Appendicitis-I: Introduction

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Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial

Sami Akbulut^1,2, Fatma Hilal Yagin², Ipek Balikci Cicek²

¹Department of Surgery, Liver Transplant Institute, Inonu University Faculty of Medicine, 244280 Malatya, Turkey.

Diagnostics (Basel, Switzerland)

|March 29, 2023

Summary

This summary is machine-generated.

This study developed a machine learning model to accurately predict acute appendicitis (AAp) and perforated AAp. Explainable AI identified key biochemical markers for improved clinical decision-making in AAp diagnosis.

Keywords:

explainable artificial intelligence machine learning nonperforated acute appendicitis perforated acute appendicitis predictive markers

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Area of Science:

Medical Informatics
Machine Learning in Medicine
Surgical Diagnostics

Background:

Acute appendicitis (AAp) diagnosis can be challenging, particularly differentiating perforated from nonperforated cases.
Accurate and timely diagnosis of AAp is crucial for effective patient management and outcomes.
Existing diagnostic methods may have limitations, necessitating advanced predictive tools.

Purpose of the Study:

To develop a highly accurate machine learning (ML) model for predicting perforated and nonperforated acute appendicitis (AAp).
To utilize explainable artificial intelligence (XAI) to understand the clinical interpretability of the ML model.
To identify key demographic and biochemical parameters influencing AAp and perforated AAp prediction.

Main Methods:

A retrospective analysis of 1797 patients with suspected AAp was conducted.
Machine learning techniques including Random Forest, Boruta, SMOTE, and CatBoost were employed for data imputation, feature selection, class imbalance resolution, and classification.
The SHAP method was used for model interpretability to identify significant predictive variables.

Main Results:

The CatBoost model achieved 88.2% accuracy in distinguishing AAp from non-AAp and 92% accuracy in differentiating perforated from nonperforated AAp.
Key biochemical markers like high total bilirubin, WBC, Neutrophil, CRP, and low PNR were significant predictors for AAp.
Elevated CRP, Age, Total Bilirubin, PLT, and WBC, along with decreased Lymphocyte and PNR, were associated with perforated AAp.

Conclusions:

A novel approach combining ML and XAI successfully predicted AAp and perforated AAp with high accuracy.
This integrated methodology provides valuable insights into the demographic and biochemical factors driving AAp and its perforation.
The study demonstrates the potential of AI-driven tools to enhance clinical decision-making in diagnosing acute appendicitis.