Optimized KNN Algorithm for Diabetic Retinopathy Classification with PCA-Based Data Fusion and Cuckoo Search Optimization

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Diabetes is a disease that occurs when the body is unable to use the insulin it produces effectively or the body fails to produce enough insulin. One of the most important complications of this disease is diabetic retinopathy (DR), which is considered the main cause of severe visual impairment and blindness. Previous studies have proven that the KNN algorithm is an effective algorithm for solving classification and prediction problems, as the performance of this algorithm rely on determining the value of the K parameter because the inappropriate choice of this value can negatively affect the accuracy of classification. On the other hand, adjusting this value manually is very difficult because this value depends on the state of determining the solution to the problem each time. Therefore, there is still an urgent need to use smart algorithms to adjust this value and obtain an ideal value that ultimately leads to obtaining a very high classification accuracy. In this paper, the Cuckoo Search algorithm was used, which is considered one of the smart and modern algorithms in the field of diagnosis, in addition to applying more than one technique and algorithm to build an integrated system to enhance the accuracy of diagnosis and obtain competitive diagnostic accuracy. The proposed work was implemented using the Debrecen diabetic retinopathy dataset and competitive results were obtained for recall, sensitivity, precision, F1 score, accuracy and specificity (98.05%), (97.30%), (99.01%), (98.70%), (99.70%), and (99.08%), respectively. Our results demonstrate that the Cuckoo Search algorithm is an effective and suitable choice for optimizing the parameters in the KNN algorithm, in addition to enhancing this algorithm to diagnose the disease early and support direct intervention and treatment, and this method lays the foundation for diagnosing other diseases and thus improving patient care in most related fields.