Effect of Hyperparameter Optimization on Artificial Learning Model Performance: Detection of Diabetes Retinopathy

Abstract

Smart systems gain importance when both the importance of early diagnosis and treatment for the patients and the costs associated with the use and maintenance of medical devices are considered. In this direction, it is necessary to obtain high-performance models for more effective smart systems. It is aimed to propose an effective hybrid prediction model in which hyperparameters are automatically determined and to lay the groundwork for a clinical decision support system to be developed for the detection of Diabetes Retinopathy. Prediction models have been designed with Extreme Learning Machine and Support Vector Machine algorithms, parameter optimization has been carried out by hybridizing these algorithms with Genetic Algorithm. ELM thought to be open to development and give effective results in terms of application, has been especially preferred. ELM achieved the highest accuracy with a value of 74.49%. The parameters of this model are as the number of neurons is 180, the activation function used is tan-sigmoid, and the threshold value used to determine the class label is 0.427. According to different performance evaluation measures, developed models are more successful than the studies carried out with the same data set in the literature.