An intelligent dashboard framework for healthcare data analysis and disease outbreak prediction

Health management systems are developed to support hospitals in managing day-to-day operations. Even though dashboard management is useful in healthcare systems, creating a dashboard is still time-consuming and challenging. Technological data analytics developments can help AMCs increase operational effectiveness and meet their financial goals. Thus, in this research, a dashboard is developed by collecting necessary information related to COVID-19-affected patients from the hospital Chennai, India. At the initial stage, the data augmentation is performed after collecting information. Following this, the dashboard is designed to present the necessary information, and data visualisation is carried out to display this information effectively. Next, recommendations are made based on the predicted number of cases using a Multilayer Perceptron (MLP) combined with a Ridge Classifier (MLP-Ridge). These recommendations address some demands such as increasing hospital facilities, vaccination, drug prescriptions, implementing lockdowns and raising public awareness. Finally, the performance of the suggested model is estimated over existing methods with different performance measures. Throughout the analysis, the outcome of the designed MLP-Ridge method attains an accuracy of 91% and an MCC of 82%. The MLP-Ridge method demonstrates enhanced performance when compared to various traditional approaches.