Abstract:
The most
widely-used method for diagnosis of osteoporosis is to determine bone
mineral
density (BMD) by bone densitometry. At present mass screening is not, on the
basis of resource constraints, considered a option.
This
project investigates if artificial neural networks (ANN s) or Baysian networks
(BNs), using the health and lifestyle history of a patient, (risk factors - used as a generic
term for
inputs) may be used to develop a preliminary screening system to determine in
a
patient is at particular risk from osteoporosis and hence in need of a scan.
Two databases have been used, one containing 486 records (29 risk factors) of patients
examined with a G E Linear Peripheral Densitometer (PIXI) and the other with 4,980
records
(33 risk factors) of patients examined with dual energy X ray absorptiometry
(DEXA).
BNs tend to
out-perform AN s particularly where smaller learning sets are involved.
The best result was 84% accuracy (sensitivity 0.89 and specificity 0.80) with PIXI and a
BN. I
general, however, with ANNs the sensitivity achieved with PIXI and DEXA
was 0.65 and 0.80 respectively and the corresponding values with BNs were 0.72 and
0.81. The
diagnostic performance with ANNs could be achieved with fewer risk factors
(PDQ from 29 to 4 and DEXA from 33 to 5) but with BNs a reduction in performance
accompanied a reduction in the number of risk factors.
l
The results also indicate:
0 For Positive
patients, the more severely affected by the disease the more
accurately they are diagnosed .
0 The lack of continuous values in the DEXA data results in a poor diagnosis of
Negative patients.
0 Classifications based on BMD predictions and pattern recognition give similar
results.
0
Reasoning with BNs can provide an indication of how a particular risk factor
state contributes to a
patient`s risk from osteoporosis.
