ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATION IN DIAGNOSIS OF ACUTE CORONARY COMPLICATIONS J L Patel

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Oct 20, 2013 (3 years and 11 months ago)

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ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATION IN
DIAGNOSIS OF ACUTE CORONARY COMPLICATIONS

J L Patel
1

, L D Patel
2
, R K Goyal
3
, C R Chaudhry
4
, K S Patel
1
, B D Antala
1
,

A M Suthar
1

1
S. K. Patel College of Pharmaceutical Education and Research, Ganpat V
idyanagar,
Mehsana, Gujarat, India
,
2
C. U. Shah College of Pharmacy and Research, A'bad
Surendranagar Highway, Wadhwan City, Sur
endranagar, Gujarat, India
,
3
L. M. College
of Pharmacy,
Gujarat, India
,
4
U. V. Patel College of Engineering, Ganpat Vidyanagar,
Mehsana
, Gujarat, India


Artificial Neural Networks (ANNs) are the mathematical algorithms generated by
computer that approach the functionality of small neural clusters in a very fundamental
manner. The artificial analogue of the biological neuron is refe
rred as a Processing
Element (PE). PEs are organized into groups referred as layers. Generally there are three
types of layers. The input layer collects information presented, the output layer generates
a response to a given input and the layers between in
put and output layers called hidden
layers. PEs in any one layer are joined with all PEs in the layer above. The neural
network must first be trained by a sufficient number of input data with output resulted
from each input data. Once trained, the neural n
etworks able to recognize similarities
when presented with a new input pattern. Trained ANNs can be used in medicine in four
basic fields: Modeling, Bioelectrical signal processing, Diagnosing and Prognostics. We
have tested the ANNs for prediction of acut
e coronary complications like myocardial
infarction and acute coronary syndromes using the diagnostic and general patient data as
inputs. Our study suggests that ANNs can predict such acute coronary complications in
suspected patients with high accuracy an
d the prediction accuracy can be improved by
optimizing number of hidden layers and the training data sets. Looking to the potential
applications of ANNs, it is emphasized that completely new diagnostic equipments can
be designed based on the neural networ
k technology that might be helpful to the
physicians for emergency diagnosis.