Emotional Intelligence-Enhanced Convlstm-Based Water Wave Optimization for Healthcare Data Analysis

Artificial intelligence (AI) and sophisticated optimisation techniques have shown to be revolutionary in the field of healthcare applications, particularly in the domains of data analysis, prediction, and decision-making. Regarding dynamic and sophisticated healthcare data, traditional optimisation methods often provide difficulties. This work investigates in the framework of healthcare the application of Emotional Intelligence (EI) and Convolutional Long Short-Term Memory (ConvLSTM) networks inside a Water Wave Optimisation (WWO) algorithm. This work aims to solve the limitations associated with traditional optimisation methods. Medical records and patient monitoring among other things make analysis and prediction of the consequences of data in the healthcare sector difficult due of their often huge, unstructured, dynamic character. The complexity of this data cannot be sufficiently captured by the present optimisation methods, thus solutions derived from them are not ideal for the decision-making process in the healthcare sector. Emotional Intelligence-based Convolutional Long Short-Term Memory (ConvLSTM) integrated Water Wave Optimisation (EI-ConvLSTM-WWO) algorithm is presented in this work. By means of the simulation of emotional decision-making, the EI model aids in the optimisation process. This so enhances the convergence and accuracy of the WWO method. Spatiotemporal characteristics of healthcare data are obtained using convolutional long short-term memory.