Truth discovery in healthcare prediction using machine learning

Abstract Healthcare is one of the core areas of the medical field in today’s digital world. By analyzing large amounts of patient data, healthcare systems are needed to gain insights and help disease prediction. The truth discovery of the Health Recommendation System (TDHRS) is becoming an important platform for medical services. The proposed use of Boltzmann Machine (RBM)-FP-Growth Health Pattern Recognition (RBMG) smart HRS can provide insights into how big data analysis can be used to implement an effective health recommendation engine, and will be used in Transition from a standard solution to a more personalized paradigm in the telemedicine environment. By considering the Root Mean Square Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-FP-Growth) exhibits fewer errors than other methods.