6G Wireless with Cyber Care and Artificial Intelligence for Patient Data Prediction

The Traditional hospital manual-centric focused model has been replaced with the decentralised patient-centric approach in recent data processing models. The advent of the 6G communication model makes high data rate transmission in less time for remote healthcare services. As healthcare applications grow, data are generated in numerous forms and sizes. Due to the data rate, latency, and bandwidth complexity, the next-generation communication model, 6G, will be utilised in future healthcare domains. This research uses 6G-enabled cybercare devices to monitor the patients. The data from cyber care devices are secured with a Deep learning model. Further critical data is immediately analysed using an intelligent data analytics model, and patients are saved in time. The enhanced rectified linear unit with a feed-forward deep neural network is used to analyse and predict patient data. An improved fixed linear unit with a feed-forward neural network for analysing and predicting patient data enhances the system's predictive capabilities. Furthermore,the performance of the prediction system is improved with the evolutionary approach called Artificial Bee Colony (ABC) based feature selection by selecting the most relevant features. By training the model on historical patient data, it can learn complex relationships and make accurate predictions about future health outcomes or events, enabling proactive interventions and personalised treatment plans. In addition, the developed ABC-Feed-FDNN model provides better results compared to existing methods in terms of obtained accuracy, precision, recall and F1 score.