Medical Diagnosis Enhancements Through Artificial Intelligence

Diagnosis of diseases is the process of converting observed evidence into the names of diseases. Central to the effective delivery of health care by the physician is the complex skill of clinical problem solving. The accuracy of this skill is crucial to the life and well being of his/her patients. The efficiency with which it is applied is of great economic important. Applying Artificial Intelligence (AI) techniques in medical field may help not only in improving the accuracy performance of classification but also in saving diagnostics' time, cost, and the pain accompanying pathologies' tests. This paper introduces an evolution of AI techniques that have been used in medical diagnosis. Then, it introduces the author’s experiments using Machine Learning (ML) algorithms on Thyroid Disease Datasets with and without Feature Subset Selection (FSS). The experiments’ motivation is to determine the usefulness and the feasibility of FSS to decision making under risk (Medical field as an example).