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Public H ealth Model (proposition). Technical University of Gdansk Team. General description. Knowledge Based System – idea Construction Methodology - Object Oriented Methodology to describe input and output data Pre - processing
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Public Health Model(proposition) Technical University of Gdansk Team
General description • Knowledge Based System – idea • Construction • Methodology - Object Oriented Methodology to describe input and output data • Pre - processing • Rules - Rule description of the relations (conditions -> conclusions) • Model- Fuzzy rules model • Tuning – membership function creation • Adaptation – membership function modification
GUI Traffic accident knowledge base Human diseases knowledge base KBS system
Traffic accidents knowledge base (I) Use of OOM to decribe the risk of injuryfactors Street characteristics: lighting of main and secondary street, number of traffic conflicts, visibility and geometry of traffic system, etc Operational characteristics: approximation speed in both, main and secondary street, volume and traffic composition and waiting time at the secondary street, etc Driver perception and motor functions: vision, audition, reflex time, concentration, elevated blood alcohol, etc
Traffic accidents knowledge base (II) Street characteristics: lighting of main and secondary street, number of traffic conflicts. Operational characteristics: approximation speed in both, main and secondary street, volume and traffic composition Driver perception and motor functions: reflex time, concentration. Road types Traffic volume
Traffic accidents knowledge base (III) conditions -> conlusion road types, traffic, volume -> number of traffic accidents
Human diseases knowledge base (I) CO -> Angina - Affects pregnancies, breathing and/or cardiac problems; NOx (Nitrogen oxides)-> Bronchitis - Pneumonia; Pb (Lead)->Affects reproductive, circulatory and nervous systems; HC (hydrocarbons)-> Eyes irritation - Sneeze - Head cold - Cancerous diseases; SOx (Sulphur oxides)-> Asthma - Bronchitis - Coughing.
Human diseases knowledge base (II) Use of OOM to describe human diseases factors CO -> Angina, affects pregnancies NOx (Nitrogen oxides)-> Bronchitis, Pneumonia; Pb (Lead)->Affects reproductive, circulatory and nervous systems; HC (hydrocarbons)-> Cancerous diseases, eyes irritation,; SOx (Sulphur oxides)-> Asthma, Bronchitis Concentration of CO, NOx,, HC, SOx Pb
Human diseases knowledge base(III) Conditions -> conlusion Concentration of CO, NOx, HC, SOx Pb Human diseases
Formal description we assume that the divalent linguistic values will adopt values from the sets trivalent values from the set {0,1,2}or {small, medium, big }
Number of rules • the number of the rules will be as follows (for human diseasesexample): R=r^k=5*5*5=125 r - number of input data k- number of fuzzy sets
INFERENTION Membersip function for y DEFUZZYFICATION Sharp value for y FUZZYFICATION Mebership functions for u1,u2 Ai(u1) u1 y Ck(y) Bj(u2) u 2 Fuzzy modeling
gt P(u1l,u2l,y3l) gt System Model ( gt) ĝt (ĝt) ĝt Adaptation procedures