[Study on the application of artificial neural network on diabetes mellitus/insulin-glucose tolerance classification].
OBJECTIVE
To discuss the potential application of artificial neural network (ANN) on the epidemiological classification of disease.


METHODS
Learning vector quantification neural network (LVQNN) and discriminate analysis were applied to data from epidemiological survey in a mine in 1996.


RESULTS
The structure of LVQNN was 25-->13-->3. The total veracity rates was 96.98%, and 92.45% among the abnormal blood glucose individuals. Through stepwise discriminate analysis, the discriminate equations were established including 11 variables with a total veracity rate of 87.34%, but was 85.53% in the abnormal blood glucose individuals. Further analysis on 30 cases with missing values showed that the disagreement ratio of LVQ was 1/30, lower than that of discriminate analysis of 7/30.


CONCLUSIONS
Compared to the conventional statistics method, LVQ not only showed better prediction precision, but could treat data with missing values satisfactorily plus it had no limit to the type or distribution of relevant data, thus provided a new powerful method to epidemiologic prediction.
Qian
Ling
L
National Health Education Institute, Chinese Center for Disease Control and Prevention, Beijing 100011, China.
Shi
Lv-yuan
LY
Cheng
Mao-jin
MJ
D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D002681:China / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000097:blood / Q000145:classification* / Q000453:epidemiology; D005951:Glucose Tolerance Test; D006801:Humans; D007328:Insulin / Q000097:blood / Q000378:metabolism*; D000078790:Insulin Secretion; D016015:Logistic Models; D016571:Neural Networks, Computer
D001786:Blood Glucose; D007328:Insulin