A Novel Security Model for Healthcare Prediction by Using DL

Predictive models are employed in order to forecast forthcoming events of which knowledge is now lacking. This is achieved by analysing a collection of pertinent predictors or variables, taking into account both contemporary and past data. Predictive modelling, alternatively referred to as predictive analytics, encompasses the utilization of statistical methodologies, data mining techniques, and artificial intelligence approaches to address a diverse range of applications. In the field of healthcare, a predictive model is utilized to acquire knowledge from past patient data in order to forecast future medical issues and subsequently select the most appropriate course of therapy. This review emphasizes the application of deep learning (DL) models, including LSTM-Bi-LSTM, RNN, CNN, RBM, and GRU, in various healthcare contexts. The findings suggest that the LSTM/Bi-LSTM model is commonly employed in the analysis of time-series medical data, whereas CNN is frequently utilized for the examination of medical picture data. The utilization of a model based on deep learning has the potential to support healthcare personnel in expediting decision-making processes pertaining to prescriptions and hospitalizations, resulting in time savings and enhanced service provision within the healthcare business; especially for COVID-19 case, this model can be used which makes it better suitable for examining medical images. The present study examines the many prediction models employed in healthcare applications through the utilization of deep learning techniques and also makes sure that the extracted dataset is secured while processing it.