Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

Rule 3: IF symptoms are
F ( P (F / CP) = 0.6),
R (P (R/ CP) = 0.9),
HBA ( P (HBA / CP) =0.8)
THEN the patient hopefully bears CP.
We compute P(CP/ R, F, HBR), P(T / F, HBA) and P(GM /R, F, HBA) and
then find the highest among them and hence draw a conclusion in favor of the
disease with the highest probability. Now,
P (CP /R, F, HBA) =
P(R/CP)}
(9.10)
P (CP) P (R/ CP) P (F / CP) P (HBA / CP)
∑ P (F /x) P (HBA /x) P (x) {P (R/GM) +
x ε {CP,GM, T}
Analogously, we can compute P(GM / R, F, HBA) and P(T / F, HBA).
It is to be noted that the denominator of (9.10) is common to the three
conditional probabilities. So, evaluation of their numerators only is adequate
for comparison.
Now, P (CP) .P (R/CP). P (F/CP). P(HBA/CP)
= 0.5 x 0.9 x 0.6 x 0.7 = 0.189
P (GM). P (R/GM). P (F/GM). P(HBA/GM) = 0.134
and P (T). P (F/T) . P (HBA/T) = 0.108
From the above results it is clear that the patient bears CP. The system
would respond to the user in the following manner. “It is highly probable that
the patient bears CP for P (CP/ conditional events) is the highest among the
competitive conditional probabilities for other diseases.”
Limitation of Bayesian reasoning
n
1. Since ∑ P (Hj) = 1, if a new hypothesis is discovered,
j=1
P(Hj) for all 1 ≤ j ≤ n+1 then have to be redefined by an expert
team.