This paper compares three different data analysis methods in a subfield of the coronary heart disease risk assessment (CHDRA) area - the identification of increased blood cholesterol levels. A data set containing the cholesterol data of 166 persons is employed as a test case, and analyzed in three experimental investigations. The first analysis method employs a predominantly knowledge-based fuzzy expert system solution to the problem. The second method employs statistical discriminant analysis on the same data, whereas the results of the third analysis are obtained by an artificial neural network. This study evaluates the advantages as well as the disadvantages of each method. Special attention is given to the systems' individual capability for managing the uncertainty involved in the decision-making process. The results achieved in the study provide evidence for the complementary and mutually supporting character of the different approaches.
|ジャーナル||International Journal of Intelligent Systems|
|出版ステータス||Published - 2000 2月|
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