A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence

Lalithadevi, B.; Krishnaveni, S.; Gnanadurai, J. Samuel Cornelius

doi:10.1007/s10916-023-01976-7

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…Machine-learning and deep-learning models also need a high computing cost during model training. To improve the interpretability and transparency of these black box models, explainable AI models are currently used in various studies [38][39][40][41][42][43][44][45][46][47]. The collaboration of experts from biology, computer science and different fields can improve the transparency of such methods.…”

Section: Challenges In ML and Dlmentioning

confidence: 99%

“…Several explainable AI models have been developed for biomedical applications such as MRI scan images to predict the survival of brain tumours [38], ECG data to predict cardiovascular disorders [39] and risk factor identification of diabetic retinopathy [40]. In these studies, SHAP analysis has been incorporated to the AI models to interpret the outcome of the classifier.…”

Section: Explainable Aimentioning

confidence: 99%

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From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications

Gromiha,

Preethi,

Pandey

2024

BioMedInformatics

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Section: Challenges In ML and Dlmentioning

confidence: 99%

Section: Explainable Aimentioning

confidence: 99%

From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications

Gromiha,

Preethi,

Pandey

2024

BioMedInformatics

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“…In the study of Lalithadevi et al [13], titled "A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence," the authors investigate the use of explainable artificial intelligence (XAI) for the early detection of diabetic retinopathy in individuals with Type II diabetes. Their research aims to develop a system that not only detects diabetic retinopathy but also provides explanations for its predictions, enhancing transparency and clinical utility.…”

Section: Related Workmentioning

confidence: 99%

Feature Importance Analysis for Glucose Level Detection in Type 2 Diabetes using Machine Learning

Rao,

Hussain

2023

ISI

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In this paper, we present a machine learning-based approach for the detection of glucose levels in type 2 diabetes patients. Our approach utilizes physiological parameters such as Body Mass Index (BMI), age, sex, and blood pressure, along with glucose levels, to train a predictive model. A dataset comprising demographic information, clinical history, and laboratory test results of 500 type 2 diabetes patients was collected for training and validation. Logistic regression, support vector machines, and random forest classifiers were trained and evaluated using various performance metrics, including accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic (ROC) curve. Results showed that the random forest classifier outperformed the other models, achieving an accuracy of 85% and an AUC-ROC score of 0.90. Feature importance analysis identified age, BMI, and blood pressure as the most critical predictors for glucose level detection in type 2 diabetes patients. Our proposed machine learning-based approach demonstrates promising results for the accurate detection of glucose levels in type 2 diabetes patients. It has the potential to assist healthcare professionals in making timely and accurate decisions regarding diabetes management. Furthermore, our findings provide valuable insights into the essential predictors for glucose level detection, which can guide future research in this area.

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“…Therefore, XAI-based research is needed to improve understanding of the complex pathogenesis of DR and potentially improve diagnostic and treatment strategies. Implementing XAI-based models could not only illuminate previously elusive biomarkers but could also significantly enhance diagnostic precision and contribute to more effective, individualized treatment strategies [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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

Yagin,

Yasar,

Gormez

et al. 2023

Metabolites

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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.

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A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence

Cited by 6 publications

References 44 publications

From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications

From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications

Feature Importance Analysis for Glucose Level Detection in Type 2 Diabetes using Machine Learning

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

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