Medical decision making based on inductive learning method

Medical decision making based on inductive learning has been studied in order to collect experience necessary for practical use of such methods in clinical and epidemiological work. The decision trees have been constructed by using the modified Quinlan's approach based on choosing relevant attributes according to their informativity. An inductive learning software tool, ASSISTANT Professional, has been used for experimenting. The variability in results has been studied under varying learning conditions. Two sets of data have been chosen for learning experiments: from a study on rheumatoid factors in patients with rheumatoid arthritis, and from an epidemiological investigation of aging. The results of this study indicate the necessity to determine inductive learning parameters for each particular problem. The pruning procedure is always recommended as it eliminates redundant elements in the tree. In problems with greater number of attributes, however, pruning itself is not guaranteeing satisfactory solutions. Interventions like the change of the minimal weight threshold might improve the situation. If these precautions are met, the method of inductive learning seems to be a useful guide in practical clinical and epidemiological decisions.