Development of risk reduction for health promotion strategy based on data-mining

2003 Fiscal Year Final Research Report Summary

• Project/Area Number: 13470099
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Public health/Health science
• Research Institution: St. Marianna University School of Medicine
• Principal Investigator: YOSHIDA Katsumi YOSHIDA,Katsumi, 医学部, 教授 (80158435)
• Co-Investigator(Kenkyū-buntansha): ICHIMURA Takumi Hiroshima City University, Information Science, Instructor, 情報科学部, 助手 (10295842) ; SUKA Machi ST.MARIANNA UNIVERSTIY SCHOOL OF MEDECINE, Medicine, Instructor, 医学部, 助手 (30339858) ; SUGIMORI Hiroki ST.MARIANNA UNIVERSTIY SCHOOL OF MEDECINE, Medicine, Assistant Professor, 医学部, 講師 (20276554)
• Project Period (FY): 2001 – 2003
• Keywords: risk reduction / data-mining / neural network

Research Abstract

Because of the recent change in disease structure in Japan, health policies such as Healthy Japan 21 or Health Promotion low were introduced for he prevention of lifestyle-related diseases. In this circumstance, not only screening to diseases but also risk reduction are expected to be valuable in health policy. Development of risk reduction model requires the early change of health status and evaluation of longitudinal change. However, the ordinary risk analysis methods have the limitation for the detection of the early changes.
This study conducted the non-linear methods based on data-mining for assessment of the risk reduction strategies. Genetic algorism and immune-system algorism were also included in this projects. Comparison of epidemiological analysis such as proportinal hazard model, neural network modes were performed for the etiological analysis for ICU acquired infections. Aggregate single point model, multiple point model and proportional hazard model are compared in the area under the curve of ROC and classification accuracy. This result showed the multiple point model were superior to proportional hazard model and aggregate single point model. For development of risk reduction model, non-linear model seems to be appropriate for data-mining strategies. The cimposition of the database also seems to be important for the proper knowledge acquisition. Concordance index proposed by Harell et al will be matter under consideration for the comparison of the priority to some models.