Fuzzy logic best represents causation for disease process and physician behavior

We hypothesize that the mechanism of normal and pathogenic variable interactions in nature, that is, the causal process involved in disease and its treatment, is best represented by fuzzy logic (FL) rather than probability theory. The key concept is causality. In medicine, physician decisions regarding diagnosis and treatment must be based on the understanding of causal mechanisms in human physiology. The causal mechanism in nature is best measured by FL when compared to traditional probability based statistics because: 1) FL allows for the lack of constraint on variable value range when considered in the context of other variables; 2) FL does not require the separation of variables from the object of interest; 3) the fuzzy hypercube allows for generation of new variables in the context of old and for an easily visualized measure of causality; and 4) Hume showed causation cannot be directly observed and there is an irreducible element of uncertainty about what causes what. Since probability is a measure of certainty, it is less equipped to measure and describe complex causal interactions than is fuzzy logic