An increasing application of machine learning classification algorithms is medical diagnose. Diagnosing if a patient has a specific disease is a simple binary classification problem.
For example, we may build a model to identify if a patient has Hepatitis C. In this case, there are two possible outputs: yes and no, which is a typical binary classification problem.
Thus, the big challenge for medical diagnosis is collecting the correct data to train a model for a specific disease: blood tests, x-ray images, ultrasound, etc.
Each of these data types can help diagnose and need particular treatment before used to feed ML models.
Finally, we’re far from a general artificial intelligence system capable of replacing doctors. However, the excellent results obtained by applying ML models to clinical data show that ML will be a powerful tool for healthcare.