Synergizing Coati Optimization and Hybrid Neural Architecture for Optimized Psychological Disease Prediction

Predicting psychological diseases using machine learning has gained prominence in healthcare informatics. This research introduces an Optimized Enhanced Hybrid Approach (OEHA) by integrating the Coati Optimization Algorithm with the previously proposed Hybrid Hopfield Recurrent Neural Network (H2RN2) model. This synergy aims to optimize feature selection in psychological disease prediction, ultimately leading to improved accuracy and efficiency. This research also proposes an enhanced hybrid approach that combines the power of Convolutional Long Short-Term Memory (ConvLSTM) with hybrid hopfield attention layer to predict psychological diseases through analysis of clinical Electronic Health Records (EHRs). This approach can handle the multimodal data by using ConvLSTM while addressing the challenges LSTM alone might struggle to effectively capture spatial information in clinical data. By using attention layer with hopfield network, variable-length sequences are handled, parallel processing is achieved, vanishing gradient problem is vanished, leading to accurate prediction.