Symptom Based Disease Prediction Using Machine Learning

The Disease Prediction Method uses predictive modeling to predict the user's disease based on the symptoms that the user offers as feedback to the system. Medical servicesare in desperate need to be advanced in order to make better choices about patient care and treatment options. In terms of machine learning, Healthcare enables humans to process large and complex medical databases, interpret them, and derive clinical insights. The machine analyzes the user's symptoms asinput and returns the disease's likelihood as an output. Implementing the Decision Tree, K Nearest Neighbor, Naïve Bayes and Random Forest allows for disease prediction. In this paper, we attempt to integrate machine learning capabilities in health care into a single framework. Instead of diagnosis, healthcare can be made smart by implementing disease prediction using machine learning predictive algorithms. Whenan early diagnosis of a disease is not possible, certain cases may arise. As a result, disease prediction can be applied effectively. This paper focuses primarily on the creation of a scheme, or what we would call an immediate medical provision, that would integrate symptoms obtained from multisensory devices as wellas other medical data and store it in a healthcare dataset. This Dataset would be analyze using machine learning algorithm with accuracy more than 90%.