This study suggests that data-driven technology is currently affecting professional decisions in health care through making forecasts or giving advice. In recent clinical research, there are a number of examples of how machine learning can be used, especially for prediction of outcomes algorithms. These effects can be everything from death and sudden cardiac arrest to irregular heartbeats and serious kidney damage. In this piece, we describe a way to make medical decisions when there isn't enough information. Its main building block is ontology-based automatic logic, and machine learning methods are added to improve the working models of patient records in order to deal with the problem of data that is missing. In this article, we give a summary of the most recent findings on predicting outcomes from associated research that look at data processing, inference, and model evaluation models made with data taken from electronic health records. In short, we show that machine learning has the potential to help with a job that is very important to medical professionals. This is done by dealing with lost or noise patient data and making it possible to use several clinical information’s.