Early Detection of Diabetic Retinopathy Using Ensemble Learning Approach

Mehrbakhsh Nilashi, Sarminah Samad, Elaheh Yadegaridehkordi, Azar Alizadeh, Elnaz Akbari, Othman Ibrahim

Abstract


Diabetes has been one of the leading health problems in all over the world. Diabetic retinopathy is the most common retinal vascular disease. Supervised data mining techniques have been successful in detecting diabetic retinopathy through a set of datasets. However, the most methods developed by supervised methods do not support the ensemble learning of data. The aim of this paper is to take the advantages of ensemble learning and develop a new method for diabetic retinopathy using data mining techniques. We use Non-linear Iterative Partial Least Squares for data dimensionality reduction, Self-Organizing Map for clustering task and ANFIS ensemble to classify unlabeled retinal images with high accuracy. We test our method on a publicly available Messidor dataset and present our results in comparison with the latest results of previous studies. For classification task, features of retinal images used for experimental analysis have been extracted by two algorithms, anatomical part recognition and lesion detection. The experimental analysis showed that the proposed method is robust in classifying the retinal images with Accuracy= 0.915, Sensitivity=0.946 and Specificity=0.917. The results of experimental analysis also demonstrated that our method performance is superior to Neural Network (NN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Decision Trees (DT), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The hybrid intelligent system has potential to assist medical practitioners in the healthcare practice for early detection of diabetic retinopathy.


Keywords


Healthcare, Early detection, Diabetic Retinopathy, NIPALS, SOM, Ensemble Learning

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References


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