Machine Learning Solutions in Computer-Aided Medical Diagnosis

The explosive growth of medical databases and the widespread development of high performance machine learning (ML) algorithms led to the search for efficient computer-aided medical diagnosis (CAMD) techniques. Automated medical diagnosis can be achieved by building a model of a certain disease under surveillance and comparing it with the real time physiological measurements taken from the patient. If this practice is carried out on a regular basis, potential risky medical conditions can be detected at an early stage, thus making the process of fighting the disease much easier. With CAMD, physicians can trustfully use the “second opinion” of the ‘digital assistant’ and make the final optimum decision. The recent development of intelligent technologies, designed to enhance the process of differential diagnosis by using medical databases, significantly enables the decision-making process of health professionals. Up-to-date online medical databases can now be used to support clinical decision-making, offering direct access to medical evidence. In this paper, we provide an overview on selected ML algorithms that can be applied in CAMD, focusing on the enhancement of neural networks (NNs) by hybridization, partially connectivity, and alternative learning paradigms. Particularly, we emphasize the benefits of using such effective algorithms in breast cancer detection and recurrence, colon cancer, lung cancer, liver fibrosis stadialization, heart attack, and diabetes. Generally, the aim is to provide a theme for discussions on ML-based methods applied to medicine.