Predicting of Aneurysm with Learning Vector Quantization in Patients with Kawasaki Disease |
Jae Hyun Kwon, Myung Kul Yum, Nam Su Kim |
Department of Pediatrics, School of M edicine, H anyang University, Seoul, Korea |
학습벡터양자화 뉴론을 이용한 가와사키병의 관상동맥류 발생의 예측 |
권재현, 염명걸, 김남수 |
한양대학교 의과대학 소아과학교실 |
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Abstract |
Purpose : We applied Learning Vector Quantization(LVQ) in the analysis of data from Kawasaki
disease patients with coronary artery aneurysm in an attempt to achieve accurate predictions of
outcome for individual patients.
Methods : One hundred and seventy-five patients with Kawasaki disease were recruited. First,
data of 75 patients(of which 60 patients had no aneurysm and 15 patients had aneurysm) were
trained using the network. The data contained age, sex, white blood cell count, platelet count,
CRP, and serum albumin of each individual. Then, data of another 100 patients(of which 83 patients
had no aneurysm and 17 patients had aneurysm) were integrated into the previously trained
LVQ neural network. After the analysis was completed, the sensitivity, specificity, positive
predictive value, and negative predictive value were compared between the LVQ neural network
and Harada risk index.
Results : Out of 100 patients, it was evaluated that 21 patients had coronary aneurysm and 79
patients did not. Among the 21 patients, 15 patients actually did have the disease, while 77 out of
79 patient actually did not have coronary aneurysm. The sensitivity, specificity, positive predictive
value and negative predictive value were 88.2, 92.7, 71.4 and 97.4%, respectively. The values by
the conventional Harada scoring were 52.9, 92.7, 60.7 and 91.7%, respectively, and contrasting the
values above.
Conclusion : LVQ neural network can classify and predict patients who, in the future, will have
coronary artery aneurysm. This method is superior than the conventional methods in predicting
aneurysm. |
Key Words:
Kawasaki disease, Coronary artery aneurysm, Learning Vector Quantization neural |
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