Fuzzy logic is an important technique for modeling uncertainty in expert systems (i.e., in cases where inferencing of conclusion from given evidence is difficult to ascertain). This paper proposes a fuzzy expert system framework that combines case-based and rule-based reasoning effectively to produce a usable tool for Type 2 Diabetes Mellitus (T2DM) management. The major targets are on combined therapies (i.e., lifestyle and pharmacologic), and the recognition of management data dynamics (trends) during reasoning. The Knowledge base (KB) is constructed using fuzzified input values which are subsequently de-fuzziffied after reasoning, to produce crisp outputs to patients in the form of low-risk advice. The extended framework features a combined reasoning approach for simplified output in the form of decision support for clinicians. With seven operational input variables and two additional pre-set variables for testing, the results of the proposed work will be compared with other methods using similarity to expert’s decision as metrics.
|Title of host publication||Data Driven Wellness: From Self-Tracking to Behavior Change. 2013 AAAI Spring Symposium|
|Publication status||Published - 31 Mar 2013|
- fuzzy logic
- expert systems
- case-based reasoning
- rule-based reasoning
- machine learning