Remote Health Prediction System

Abstract

One of the many applications of machine learning in healthcare is the analysis of large amounts of data to reveal new therapeutic insights. Once doctors have this data, they can better serve their patients. Therefore, satisfaction can be raised by using deep learning to enhance the quality of care provided. This work aims to integrate machine learning and AI in healthcare into a single system. Predictive algorithms based on machine learning could revolutionize healthcare by allowing doctors to avoid unnecessary treatments. Various libraries, including those for machine learning algorithms, were used to develop this work. Because of its extensive library and user-friendliness, Python has emerged as the preferred language. syntax. The authors used various classification techniques to train machine learning models and then select the one that provided the best balance between accuracy and precision while avoiding prediction error and autocorrelation problems, the two main causes of bias and variance.