Evaluation of Methods Concerning Health Risk Based on Artificial Intelligence
| Project/Area Number |
01570323
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
公衆衛生学
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| Research Institution | Keio University |
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Principal Investigator |
YOSHIDA Katsumi Keio University School of Medicine Assistant Professor, 医学部, 講師 (80158435)
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| Co-Investigator(Kenkyū-buntansha) |
MUTO Takashi Keio University School of Medicine Instructor, 医学部, 助手 (30209986)
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| Project Period (FY) |
1989 – 1990
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| Project Status |
Completed (Fiscal Year 1990)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1990: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1989: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Artificial Intelligence / Prognosis / Connectionist / Risk estimation / 人工知能 / コネクショニスト / ッコネクショニスト |
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| Research Abstract |
The purpose of this study is to clarify the usefulness and the limitation of the neural network systems applied to the medical fields, especially to health risk assessment.
First years of this study is assigned to the preparation of the study environment. The following subjects were done ;
1. Extraction of experimental data from the medical database, 2. Development of the neural network program based on the error back propagation algorithm written by FORTRAN language, 3. Setup of the microcomputer for calculation of neural network program.
Second year was assigned to the comparison of the diagnostic capabilities between the neural network system and the ordinary statistical method (discriminant analysis). The data used in this study was concerning three hepatobiliary disorders. Following subjects were done ;
1. Modification of the back propagation to display the sequential error in the learning process, 2. Overall correct diagnostic rate of the external test data was 97.7% (212/217) in the neural network, and 65.4% (142/217) in the discriminant function. The diagnostic capability of the neural network was significantly higher than that of the ordinary statistical method.
In addition, neural network system required no statistical assumptions such as the normal distribution of the data. According to these finding, neural network system might be useful tools for estimating the health risks.
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Report
(3 results)
Research Products
(22 results)
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[Publications] Yoshida,K., Hayashi,Y. & Imura,A. (Barber,B., Cao,D., Qin,D. and Wagner,G. editors): "MEDINFO'89" NorthーHolland, 116-120 (1989)
Description
「研究成果報告書概要(和文)」より
Related Report
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