Machine Learning-Based Automated Medical Diagnosis for Healthcare

A sizable segment of the world’s population lacks access to quality healthcare. The success of healthcare ultimately depends on the doctor’s skill. In this study, we investigate if this knowledge may be represented as an information corpus, or as data that has been retrieved using data mining methods, particularly the Machine Learning & Deep Learning Model, to make a diagnosis. When the medical diagnosis is made widely available, coverage increases, and life quality improves. In order to determine whether inferences about the causes of various diseases can be made from the data, this paper provides an overview of machine learning approaches used in the classification of various diseases. We outline a few of our findings from the trials we ran before offering some suggestions for the future. The difference between the current state of health and an acceptable or desirable health condition is the health problem. By lowering doctor visits, hospital stays, and diagnostic testing procedures, monitoring systems are designed to lower health care expenditures. Using the data mining modeling technique, the integration of clinical decision support with computer-based patient records could decrease medical errors, increase patient safety, stop unwelcome practice variance, and improve practice outcomes. Connecting patients and doctors through a user- friendly interface will make it easier for patients to use in emergency situations.