PREDICTION OF DISEASE IN HEALTH CARE SYSTEM USING MACHINE LEARNING

The” Smart Health Prediction Using Machine Learning” system, grounded on prophetic modelling, predicts the complaint of cases druggies on the base of the symptoms that the stoner provides symptoms as an input to the system. The operation has three login options stoner/ case login, croaker login, and admin login. The device analyses the symptoms given by the stoner/ case as input and provides the liability of the complaint as affair grounded on the vaticination using the algorithm. Smart health prognostications are made by the perpetration of the Naïve Bayes Classifier. The Naïve Bayes Classifier measures the complaint chance probability by considering all its features that’s trained during the training phase. Exact interpretation of complaint data benefits early case/ stoner complaint vaticination and provides clear vision about the complaint to the stoner. After a vaticination, the stoner/ case can consult a specialist croaker using a converse consulting window. It uses machine literacy algorithms and database operation ways to prize new patterns from literal data.