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Public H ealth Model (proposition)

This proposition presents a knowledge-based system for modeling public health factors and traffic accidents. The system uses an object-oriented methodology to describe input and output data, pre-processes the data, and applies fuzzy rules for modeling. The membership functions are tuned and adapted, and a GUI is provided for modification. The system incorporates knowledge bases for human diseases and traffic accidents, including factors such as street characteristics, operational characteristics, driver perception, motor functions, and road types.

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Public H ealth Model (proposition)

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  1. Public Health Model(proposition) Technical University of Gdansk Team

  2. 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

  3. GUI Traffic accident knowledge base Human diseases knowledge base KBS system

  4. 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

  5. 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

  6. Traffic accidents knowledge base (III) conditions -> conlusion road types, traffic, volume -> number of traffic accidents

  7. 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.

  8. 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

  9. Human diseases knowledge base(III) Conditions -> conlusion Concentration of CO, NOx, HC, SOx Pb Human diseases

  10. Dynamic vectors

  11. 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 }

  12. 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

  13. The example membership function for the input variable

  14. 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

  15. gt P(u1l,u2l,y3l) gt System Model ( gt) ĝt  (ĝt) ĝt Adaptation procedures

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