Medical Diagnosis

ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and of their combinations for prognosis.

E.g. prediction of disease progression, for the extraction of medical knowledge for outcomes research, for therapy planning and support, and for overall patient management.

ML is also being used for data analysis, such as detection of regularities in the data by appropriately dealing with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and for intelligent alarming resulting in effective and efficient monitoring.

It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care.

In medical diagnosis, the main interest is in establishing the existence of a disease followed by its accurate identification. There is a separate category for each disease under consideration and one category for cases where no disease is present.

Here, machine learning improves the accuracy of medical diagnosis by analyzing data of patients.

The measurements in this Machine Learning applications are typically the results of certain medical tests (example blood pressure, temperature and various blood tests).

This can also be medical diagnostics (such as medical images), presence/absence/intensity of various symptoms and basic physical information about the patient(age, sex, weight etc.).

On the basis of the results of these measurements, the doctors narrow down on the disease inflicting the patient.