Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics

Abstract

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

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Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

“…Hence, the results of having diabetes are often too sudden, causing the individuals to have difficulty in following the treatment and pursuing lifestyle changes [ 6 ]. It is concerning because individuals with diabetes may get various health complications such as kidney disease, stroke, coronary heart disease, retinopathy [ 7 ], and emerging complications like cancer and liver disease [ 8 ]. Hence, creating awareness about the importance of rapid diagnosis in managing diabetes is crucial [ 9 ].…”

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery

“…Yagin et al proposed an explainable AI model to predict the outcomes of diabetic retinopathy. The model combining explainable boosting machine (EBM) feature selection and extreme gradient boosting (XGBoost) achieved 91.25% accuracy [9]. In this model, three tree-based ML models are examined, namely, XGBoost, Light Gradient Boosting (LightGBM), and Adaptive Boosting (AdaBoost), to classify metabolites data of T2D patients from normal control patients.…”