Abstract
A method of performing prognostic modeling of disease states is proposed. The technique uses rough sets to extract rules from a database. The data is then reformatted into a fuzzy logic template, and a learning algorithm is used to adjust the fuzzy set membership functions. The method is applied to the POSCH problem, which looks at risk factors associated with the progression of coronary artery disease. The POSCH data has several shortcomings, including a limited number of cases, correlated inputs, as well as noise on both the inputs and outcome. The problem was to predict progression of atherosclerosis in the LAD three years after baseline based on physiologic data available at baseline. The proposed rough/fuzzy set method correctly predicted progression of atherosclerotic disease in 69% of the patients, which is statistically better than neural network, rough set and logistic models performed.
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