# Metaheuristic-based gallstone classification using rotational forest explained with SHAP

## TL;DR

This paper introduces a machine learning model optimized with a metaheuristic algorithm to predict gallstones, achieving good accuracy and identifying key risk factors.

## Contribution

A novel gallstone prediction approach using a Rotational Forest classifier optimized with the Bald Eagle Search algorithm.

## Key findings

- RoF alone achieved 78% accuracy and 0.867 AUC using all features.
- RoF with BES achieved 75.78% accuracy and 0.860 AUC using only 17 features.
- CRP, Vitamin D, Obesity, HGB, and BM were identified as dominant features via SHAP and LIME analysis.

## Abstract

Cholelithiasis, commonly known as Gallstone disease, occurs when hardened deposits form in the gallbladder or bile ducts. It affects millions of people worldwide and is especially common in women. While many people may not experience any symptoms, symptomatic cases can present with acute cholecystitis and other complications such as pancreatitis and even gallbladder cancer. However, this disease presents a clinical challenge due to its variable symptoms and risk of serious complications. Therefore, early prediction of gallstones is essential for timely intervention.

Thus, our study presents a novel approach for predicting gallstones. In this study, we have presented a Rotational Forest (RoF) classifier optimized using the Bald Eagle Search (BES) algorithm for gallstone prediction based on a tabular dataset. Our research has been conducted across two frameworks: using RoF alone and using RoF with the BES algorithm.

While using RoF alone, an accuracy of 78% and an AUC of 0.867 was obtained using all features. An accuracy of 75.78% and an AUC of 0.860 were obtained for RoF with the BES algorithm using only 17 features. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analysis has distinguished CRP, Vitamin D, Obesity, HGB, and BM as the most dominant features.

Likewise, we have also compared our work with other novel works and validated the performance of our model for the prediction of gallstones.