Differential Evolution based Hyperparameters Tuned Deep Learning Models for Disease Diagnosis and Classification

摘要

With recent advancements in medical filed, the quantity of healthcare care data is increasing at a faster rate. Medical data classification is considered as a major research topic and numerous research works have been already existed in the literature. Presently, deep learning (DL) models offers an efficient method for developing a dedicated model to determine the class labels of the respective medical data. But the performance of the DL is mainly based on the hyperparameters such as, learning rate, batch size, momentum, and weight decay, which need expertise and wide-ranging trial and error. Therefore, the process of identifying the optimal configuration of the hyper parameters of a DL is still remains a major issue. To resolve this issue, this paper presents a new hyperparameters tuned DL models for intelligent medical diagnosis and classification. The proposed model is mainly based on four major processes namely pre-processing, feature extraction, classification and parameter tuning. The proposed method makes use of simulated annealing (SA) based feature selection. Then, a set of DL models namely recurrent neural network (RNN), gated recurrent units (GRU) and long short term memory (LSTM) are used for classification. To further increase the classification performance, differential evolution (DE) algorithm is applied to tune the hyperparameters of the DL models. A detailed simulation analysis takes place using three benchmark medical dataset namely Diabetes, EEG Eye State and Sleep stage dataset. The simulation outcome indicated that the DE-LSTM model have shown better performance with the maximum accuracy of 97.59%, 88.52% and 93.18% on the applied diabetes, EEG Eye State and Sleep Stage dataset.