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

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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.