Highly efficient stacking ensemble learning model for automated keratoconus screening
Muhsin, Zahra J. Ghafir, Ibrahim AlShawabkeh, M. Al Bdour, M. AlRyalat, S.A. Al-Taee, M.
Muhsin, Zahra J.
Ghafir, Ibrahim
AlShawabkeh, M.
Al Bdour, M.
AlRyalat, S.A.
Al-Taee, M.
Publication Date
2025-06-24
End of Embargo
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Rights
(c) 2025 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/)
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-05-21
Institution
Department
Awarded
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Additional title
Abstract
Background
Despite extensive research on keratoconus (KC) detection with traditional machine learning models, stacking ensemble learning approaches remain underexplored. This paper presents a stacking ensemble learning method to enhance automated KC screening.
Methods
This study utilizes a clinical dataset containing detailed corneal data from 2491 cases classified as non-KC (NKC), subclinical KC (SCKC) and clinical KC (CKC). Each cornea is represented by 79 features extracted from Pentacam imaging. Following extensive pre-processing, key corneal features that are strongly correlated with the target diagnosis are identified. These features are the keratometry of the steepest anterior point, surface variance index, vertical asymmetry index, height decentration index, and height asymmetry index. A novel stacking ensemble model is developed using the selected features to improve corneal classification into NKC, SCKC, and CKC by integrating top tree-based classifiers (random forest, gradient boosting, decision trees) with a support vector machine meta-classifier.
Results
The pre-processing and feature selection techniques reduced the model's parameters to just 6.33% of the original dataset, improving classification performance, and cutting over 85% of the training time. The performance of the developed model was validated and tested on unseen data. Experimental results showed that the model outperforms existing studies, achieving 99.72% accuracy, precision, sensitivity, F1, and F2 scores, with a Matthews correlation coefficient of 0.995. It accurately classified all NKC and CKC cases, with just one misclassification involving an SCKC case. The model also demonstrated consistent performance on 100 additional unseen test cases, underscoring its generalizability and robustness in KC screening.
Conclusions
By combining the strengths of diverse base models and key Pentacam indices, the stacking ensemble approach ensures reliable, accurate KC screening, providing clinicians with an automated tool for early detection and better patient management.
Version
Published version
Citation
Muhsin ZJ, Qahwaji R, Ghafir I et al (2025) Highly efficient stacking ensemble learning model for automated keratoconus screening. Eye and Vision. 12: 45.
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Type
Article