Health Status Prediction using ML Techniques

In healthcare management, a large number of multidisciplinary statistics on those affected are generated from scientific reports, physician records. However, the assessment of health parameters and the prediction of subsequent fitness situations is only at the informational stage. In this case, a series of probabilistic statistical mechanisms are designed and correlation evaluation of these cumulative statistics is performed. Finally, a random prediction version of using a knowledge-gathering utility about a set of rules called Random Forests and Neural Networks designed to predict fitness of mostly maximal correlators based on current physical popularity. Within this framework, the health prevalence of patients is expected mainly based on their BMI (body mass index) statistics. These statistics are analyzed and are based primarily on what the reputation of the physical condition of the person concerned is expected to be. Performance evaluation of the proposed protocols is achieved through well-sized simulations.