Parkinson disease prediction using improved crayfish optimization based hybrid deep learning

Abstract

Background Predicting the course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes. Objective This study presents a novel method for Parkinson's disease prediction using freely accessible resources. The suggested approach starts with band-pass filter data preprocessing and uses Empirical Mode Decomposition (EMD) for feature extraction. Then, for classification, these features are supplied into an Attention-based Efficient Bidirectional Network (ImCfO_Attn_EffBNet) based on Improved Crayfish Optimization. EfficientNet-B7, BiLSTM, and Attention modules are integrated by ImCfO_Attn_EffBNet to effectively gather temporal and geographic data. Methods Additionally, we use the Improved Crayfish Optimization (ImCfO) algorithm to maximize convergence rates, optimize the loss function, and find the global best solutions. Results ImCfO enhances performance by adding a self-adaptation criterion to the traditional crayfish algorithm. The classifier's configurable parameters are adjusted using the ImCfO resultant solution, which raises the prediction accuracy overall. Conclusion Based on a number of assessments, the ImCfO_Attn_EffBNet analyzed the performance and found that the results were as follows: accuracy (95.068%), recall (92.948%), specificity (92.89%), F-Score (92.89%), precision (92.89%), and FPR (2.1%), in that order.