title: [Study on the application of artificial neural network in analysing the risk factors of diabetes mellitus].
abstract: OBJECTIVE
  To study the use of neural network in determining the risk factors of diseases.
  
  
  METHODS
  With back-propagation neural network (BP network) as fitting model based upon data gathered from an epidemiological survey on diabetes mellitus and under the network structure of 22-6-1, the mean impact value (MIV) for each input variables and sequencing the factors according to their absolute MIVs were calculated. The results from BP network with multiple logistic regression analysis and log-linear model for united actions between factors were compared with optimizing Levenberg-Marquardt algorithm.
  
  
  RESULTS
  By BP network analysis, the sequence of importance for the risk factors of diabetes mellitus became: faster pulse, diabetes mellitus family history, living longer in the investigated area, with medical record of nephropathy, having higher ratio for waist-to-hip, being male, with medical records of diseases as hyperlipoproteinmia, coronary heart disease, hypertension, high diastolic pressure, higher income, do no drink alcohol, age, higher systolic pressure, less educated, body mass index, with medical records of other diseases, physical exercise related to jobs smoking, occupation, with medical record for cerebrovascular disease, with medical record for liver disease etc. However, only 7 factors were statistically significant in multiple logistic regression analysis. The sequence of their importance appeared as: pulse, diabetes mellitus family history, the medical record of nephropathy, waist-to-hip ratio, the medical record of hypertension, work-place related exercise and age. The sequences of importance were almost the same between the two while the difference could partly be explained by the interaction among risk factors through log-linear model.
  
  
  CONCLUSIONS
  Neural network could be used to analyze the risk factors of diseases and could assimilate more complicated relationships (main effects and interactions) between inputs and outputs, better than using the traditional methods.
authors.affiliation: Postdoctoral Station in Clinical Medicine, Jinan University, Guangzhou 510632, China. gwps@ynmail.com
authors.forename: Wei
authors.initials: W
authors.lastname: Gao
authors.forename: Sheng-Yong
authors.initials: SY
authors.lastname: Wang
authors.forename: Zi-Neng
authors.initials: ZN
authors.lastname: Wang
authors.forename: Lü-Yuan
authors.initials: LY
authors.lastname: Shi
authors.forename: Fu-Xia
authors.initials: FX
authors.lastname: Dong
mesh_terms: D000328:Adult; D002681:China / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000453:epidemiology* / Q000209:etiology*; D005192:Family Health; D005260:Female; D006801:Humans; D006949:Hyperlipidemias / Q000150:complications; D016015:Logistic Models; D008297:Male; D016571:Neural Networks, Computer; D009765:Obesity / Q000150:complications; D011674:Pulse; D012307:Risk Factors