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The study aims to enhance the diagnostic accuracy of celiac disease through machine learning techniques and feature selection methods.

May 27, 2026Journal of Electrical and Computer Engineering0 citationsOpen Access

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Hybrid Feature Selection–Based Approach to Predict Celiac Disease Using Ensemble Machine Learning With Hyperparameter Tuning and Model Explainability

KUKhandaker Mohammad Mohi Uddin MBMd. Tofael Ahmed Bhuiyan MHM M Ul Hassan

Key Points

The study aims to enhance the diagnostic accuracy of celiac disease through machine learning techniques and feature selection methods.
Utilized a random forest-based stacking ensemble approach to predict celiac disease.
Integrated hyperparameter tuning with GridSearchCV and K-fold cross-validation.
Evaluated model interpretability using SHAP and LIME methods.
Achieved 99.72% accuracy using the random forest ensemble method.
Obtained 98.5% diagnostic accuracy with the GEO dataset (GDS3646, GPL6104, GSE11501).
Identified challenges in integrating machine learning techniques into current medical protocols.

Abstract

Celiac disease is a chronic autoimmune disorder that primarily affects individuals genetically predisposed to it, causing damage to the small intestine upon gluten consumption. Although it affects around one person out of every hundred worldwide, celiac disease is frequently misdiagnosed. Eliminating serious health problems from celiac disease requires an early and precise diagnosis, and modern diagnostic techniques hold great promise for increasing diagnostic precision. Still, there is a dearth of comprehensive review literature that provides insights into upcoming developments and summarizes the state of the subject now. The diagnostic utility of machine learning algorithms, biomarker analysis, and imaging methods for celiac disease is evaluated critically in this study. The focus is on the remarkable 99.72% accuracy obtained with a random forest–based stacking ensemble method and the 98.5% diagnostic accuracy obtained with the GEO dataset (GDS3646, celiac disease: primary leukocytes, GPL6104, GSE11501). Additionally, the investigate methods to enhance model interpretability by integrating SHapley filter, embedded, and wrapper techniques are combined with a hybrid feature selection strategy improved using GridSearchCV and K‐fold cross‐validation and SHapley Additive exPlanations (SHAP), as well as Local Interpretable Model‐agnostic Explanations (LIME). The noninvasive attraction of biomarker evaluation, the merits of sophisticated diagnostic procedures, and the potential of machine learning in expediting celiac disease diagnosis are all highlighted in this paper. The argumentation also includes challenges related to integration into medical protocols and standards. Future studies will concentrate on improving testing techniques for widespread clinical use, integrating protein and genetic information to improve early diagnosis and tailoring therapies to improve the lives of patients.

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Hybrid Feature Selection–Based Approach to Predict Celiac Disease Using Ensemble Machine Learning With Hyperparameter Tuning and Model Explainability

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