Predicting visual impairment in type 2 diabetes using key performance indicators identified by the Black Widow and Whale Optimization Algorithms: A case study in Saudi Arabia
- 1Department of Mathematics and Statistics, College of Sciences, University of Jeddah, Jeddah, Saudi Arabia.
- 2School of Science, RMIT University, Melbourne, Victoria, Australia.
- 3Department of Medicine, King Abdulaziz University Hospital, Jeddah, Saudi Arabia.
Abstract
Visual impairment (VI) is a severe and prevalent complication of type 2 diabetes (T2D), particularly in high-risk populations, underscoring the critical need for early predictive strategies. A retrospective cross-sectional study was conducted to develop and validate machine learning (ML) predictive models for VI among patients with T2D in Saudi Arabia. To the best of our knowledge, this is the first study to employ WOA and BWO as feature selection techniques to identify the most informative predictors of VI among Saudi patients with T2D. Predictive models were subsequently developed and compared using both clinically recommended features and algorithmically selected features derived from WOA and BWO to determine the optimal feature set for predictive performance. Medical records of 1,000 T2D patients (555 VI [55.5%]; 445 non-VI [44.5%]) from King Abdulaziz University Hospital were analyzed. Models were developed using an 80/20 stratified train-test split, with 5-fold cross-validation performed on the training set for internal validation. Patients were divided into two groups: those with HbA1c ≥ 6.5% (full dataset) and a clinically relevant high-risk subgroup with HbA1c > 8% (reduced dataset), which incorporated 19 recommended demographic, lifestyle, and lipid profile features. Models utilizing the reduced dataset consistently achieved superior performance across all algorithms. Among them, the WOA-CatBoost model demonstrated the highest performance (Accuracy = 0.95, AUC = 0.96, 95% CI: 0.94–0.98) using only 9 features, outperforming both the BWO-Random Forest (BWO-RF) model (Accuracy = 0.92, AUC = 0.90, 95% CI: 0.87–0.93) with 10 features and the baseline Multi-Layer Perceptron (MLP) model (Accuracy = 0.91, AUC = 0.94, 95% CI: 0.91–0.97) with 13 features. Bonferroni-corrected paired t-tests based on five-fold cross-validation accuracies (α = 0.017) confirmed a significant performance advantage of WOA-CatBoost over BWO-RF (t = 10.17, p < 0.001), supporting WOA-CatBoost as the overall best-performing model. Key predictors identified across all three best-performing models included Hemoglobin A1c (HbA1c), body mass index (BMI), age, occupation, triglycerides (TG), type of food, and smoking. These findings demonstrate that WOA-CatBoost provides a clinically actionable, parsimonious predictive tool for VI risk stratification in T2D patients within the Saudi Arabian healthcare context.
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BibTeX
@article{alhussein2026predicting,
title = {{Predicting visual impairment in type 2 diabetes using key performance indicators identified by the Black Widow and Whale Optimization Algorithms: A case study in Saudi Arabia}},
author = {Faten Al-Hussein and Laleh Tafakori and Mali Abdollahian and Khalid Al-Shali},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361022},
url = {https://doi.org/10.1371/journal.pone.0361022},
note = {CC BY},
}
RIS
TY - JOUR
TI - Predicting visual impairment in type 2 diabetes using key performance indicators identified by the Black Widow and Whale Optimization Algorithms: A case study in Saudi Arabia
AU - Faten Al-Hussein
AU - Laleh Tafakori
AU - Mali Abdollahian
AU - Khalid Al-Shali
PY - 2026
DA - 2026/10/21
JO - PLOS One
DO - 10.1371/journal.pone.0361022
UR - https://doi.org/10.1371/journal.pone.0361022
AB - Visual impairment (VI) is a severe and prevalent complication of type 2 diabetes (T2D), particularly in high-risk populations, underscoring the critical need for early predictive strategies. A retrospective cross-sectional study was conducted to develop and validate machine learning (ML) predictive models for VI among patients with T2D in Saudi Arabia. To the best of our knowledge, this is the first study to employ WOA and BWO as feature selection techniques to identify the most informative predictors of VI among Saudi patients with T2D. Predictive models were subsequently developed and compared using both clinically recommended features and algorithmically selected features derived from WOA and BWO to determine the optimal feature set for predictive performance. Medical records of 1,000 T2D patients (555 VI [55.5%]; 445 non-VI [44.5%]) from King Abdulaziz University Hospital were analyzed. Models were developed using an 80/20 stratified train-test split, with 5-fold cross-validation performed on the training set for internal validation. Patients were divided into two groups: those with HbA1c ≥ 6.5% (full dataset) and a clinically relevant high-risk subgroup with HbA1c > 8% (reduced dataset), which incorporated 19 recommended demographic, lifestyle, and lipid profile features. Models utilizing the reduced dataset consistently achieved superior performance across all algorithms. Among them, the WOA-CatBoost model demonstrated the highest performance (Accuracy = 0.95, AUC = 0.96, 95% CI: 0.94–0.98) using only 9 features, outperforming both the BWO-Random Forest (BWO-RF) model (Accuracy = 0.92, AUC = 0.90, 95% CI: 0.87–0.93) with 10 features and the baseline Multi-Layer Perceptron (MLP) model (Accuracy = 0.91, AUC = 0.94, 95% CI: 0.91–0.97) with 13 features. Bonferroni-corrected paired t-tests based on five-fold cross-validation accuracies (α = 0.017) confirmed a significant performance advantage of WOA-CatBoost over BWO-RF (t = 10.17, p < 0.001), supporting WOA-CatBoost as the overall best-performing model. Key predictors identified across all three best-performing models included Hemoglobin A1c (HbA1c), body mass index (BMI), age, occupation, triglycerides (TG), type of food, and smoking. These findings demonstrate that WOA-CatBoost provides a clinically actionable, parsimonious predictive tool for VI risk stratification in T2D patients within the Saudi Arabian healthcare context.
SN - 1932-6203
ER -