LEARNING FOR PATTERN CLASSIFICATION USING MULTI-OBJECTIVE PROGRAMMING AND ITS APPLICATON TO DIAGNOSIS SUPPORT SYSTEM OF DIABETIC ANGIOATHY

多目标规划学习模式分类及其在糖尿病血管病诊断支持系统中的应用

• 批准号: 06680414
• 负责人: NAKAYAMA Hirotaka
• 金额: $ 1.41万
• 依托单位: KONAN UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for General Scientific Research (C)
• 财政年份: 1994
• 资助国家: 日本
• 起止时间: 1994 至 1995
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-06680414/
• 关键词: pattern classification; neural networks; committee machine; multi-objective programming; diagnosis support system; diabetic macroangiopathy; diabetic microangiopathy; risk factors; 多目的決定; 線形計画法; 機械学習; 糖尿病; 大血管障害; 細小血管障害; 統計解析

In order to construct a diagnosis support system of diabetic angiopathy, we examined characteristic features of risk factors for macroangiopathy in 899 Japanese NIDDM with and without macroangiopathy. They were registered from 40 facilities by Multiclinical Study for Diabetic Macroangiopathy group. Three hundred eighty six subjects were identified as having macroangiopathy (MA (+) total) ; these includes 217 with ischemic heart disease (IHD), 169 with cerebrovascular disease (CVD), and 77 with peripheral vascular disease (PVD). Univariate and multivariate analyzes revealed the following factors for MA (+) total, IHD,CVD and PVD : age, fast blood sugar, hypertension, systolic blood pressure, diastolic blood pressure, duration of diabetes, diabetic microangiopathy, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), LDL-C : HDL-C ratio, brinkman index (smoking) and body mass index. In conclusion, in NIDDM patients, age, hypertension, systolic blood pressure, diastolic blood pressure and duration of diabetes were found to be risk factors for macroangiopathy.Technologies of machine learning is applied for supporting diagnosis. We developed two kinds of methods for pattern classification. One of them is a method for getting a piecewise linear discrimination function using fuzzy and/or multi-objective linear programming. The other is a committee machine which is a complex neural network consisting of several submodular neural networks. It has been observed that both methods can be effectively applied to our diagnosis problem.

为了构建糖尿病血管病的诊断支持系统，我们检查了899个日本NIDDM的危险因素的特征特征，伴有有和没有大型神经病。它们是通过多利通理研究从40个设施中注册的，用于糖尿病宏观病组。三百八十六名受试者被确定为具有宏观病（MA（+）总数）；其中包括217例患有缺血性心脏病（IHD），169例脑血管疾病（CVD）和77例外周血血管疾病（PVD）。单变量和多变量分析揭示了MA（+）总数，IHD，CVD和PVD的以下因素：年龄，快速血糖，高血压，收缩压，舒张压，糖尿病，糖尿病性糖尿病持续时间，糖尿病性微动性，高密度Lipoprotototein胆固醇（HDL-cholterl-cholsterol（HDL-C） LDL-C：HDL-C比，Brinkman指数（吸烟）和体重指数。总之，在NIDDM患者中，发现糖尿病的年龄，高血压，收缩压，舒张压和糖尿病持续时间是大型疾病的危险因素。机器学习技术用于支持诊断。我们开发了两种用于模式分类的方法。其中之一是一种使用模糊和/或多目标线性编程获得分段线性歧视函数的方法。另一台是委员会的机器，它是一个复杂的神经网络，由几个少数superdular神经网络组成。已经观察到，这两种方法都可以有效地应用于我们的诊断问题。

项目成果

Y.Hattori: "Properties of Subordinate Relations Represented by Adjacency Matrices with Plural Connected Components" Transactions of the Institute of Systems, Control and Information Engineers. Vol.8, No.11. 665-666 (1995)

Y.Hattori：“由具有多个连通分量的邻接矩阵表示的从属关系的属性”系统、控制和信息工程师学会的交易。

Y.Hattori: "Adjacency Matrix Representations of Elementary Subordinations" Transactions of the Institute of Systems, Control and Information Engineers. Vol.7, No.12. 531-532 (1994)

Y.Hattori：“基本从属关系的邻接矩阵表示”系统、控制和信息工程师研究所的交易。

Hirotaka Nakayama: "Aspiration level approach to interactive multi-objective programming and its applications" Advances in Multicriteria Analysis ed. by P. M. Pardalos,Y. Siskos and C. Zopounidis.Kluwer. 147-174 (1995)

Hirotaka Nakayama：“交互式多目标编程的愿望水平方法及其应用”多标准分析进展编辑。

服部雄一: "均等な影響力を与える従属関係の隣接行列表現" 甲南大学紀要理学編. 42. 195-199 (1995)

Yuichi Hattori：“给予同等影响力的依赖关系的邻接矩阵表示”Konan University Bulletin of Science 42. 195-199 (1995)。

Hirotaka Nakayama: "Engineering Applications of Multi-objective Programming : Recent Results" Multiple Criteria Decision Making,ed. by G. H. Tzeng,H. F. Wang,Springer. 369-378 (1994)

Hirotaka Nakayama：“多目标规划的工程应用：最新结果”多标准决策，编辑。