@misc{oai:mie-u.repo.nii.ac.jp:00010794,
 author = {森上 斐斗 and Morikami Ayato},
 month = {Jan},
 note = {application/pdf, (1. Introduction) A genetic analysis is developing to identify single nucleotide polymorphism (SNP) associated  with lifestyle disease. Many researchers are investigating the lifestyle disease depended on one  SNP. However, almost onset influence is not clear for the combination of patient's SNPs. This object of this research is the proposal of a new risk estimation method of lifestyle disease in the consideration of the relevance of SNP and biomarkers. By using this system, a physician  can suggest lifestyle guidance more exactly to a subject. (2. Proposed Method) 2.1. Outline of System Figure.1 shows the flowchart of my proposed system. Figure 1 Flowchart of proposed system 2.2. Learning Process In learning process, the system generates diagnosis dictionaries from many sets of SNPs and biomarkers such as BMI, age, sex, smoking, blood pressure, cholesterol, HbA1c. The sets are normalized for each features scale. Diagnosis dictionaries are generated for each SNP types on specific disease. 2.3. Health Check Process To estimate onset risk, subject feature is compared to individual diagnosis dictionaries.  Individual diagnosis dictionaries are selected by subject’s SNP types. (3. Evaluation of Proposed System) 3.1. Materials for experiment Specific disease is myocardial infarction, and variable element is systolic blood pressure. Numbers of onset samples are 1461 persons and non-onset samples are 649 persons to generate diagnosis dictionaries. The number of samples is 6 persons and these biomarker values are made even. 3.2. Experimental Result Figure 2 shows that the onset risk of acute myocardial infarction is different for each subject (SNPs type) under the same systolic blood pressure. Figure 2 Experimental Result I am considering that the result would become more appropriate for medical doctors. (4. Conclusions) We proposed a new health check system using SNPs and biomarkers. We will consider the other disease.(References)[1]GenoMarker,G&GScienceCo, http://www.genomarker.jp/index.html [2] Yoshiji Yamada, Hitoshi Matsuo, Sachiro Watanabe, Kimihiko Kato, Takeshi Hibino, Kiyoshi Yokoi, Sahoko Ichihara, Norifumi Metoki, Hidemi Yoshida, Kei Satoh, Yoshinori Nozawa. : Association of a polymorphism of CYP3A4 with type 2 diabetes mellitus.Int J Mol Med, Vol.20, No.5, pp.703-707, (2007), 三重大学大学院地域イノベーション学研究科博士前期課程地域イノベーション学専攻, 39},
 title = {個人の遺伝子情報（SNP)と生活習慣病の関連性を用いた健康診断支援システム},
 year = {2013}
}