No abstract

This study proposes an intelligent data analysis approach to investigate and interpret the distinctive factors of diabetes mellitus patients with and without ischemic (non-embolic type) stroke in a small population. The database consists of a total of 16 features collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, high density lipoprotein, triglyceride levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease, myocardial infarction rate, glucose level, medication and blood pressure. Metric and non-metric features are distinguished. First, the mean and covariance of the data are estimated and the correlated components are observed. Second, major components are extracted by principal component analysis. Finally, as common examples of local and global classification approach, a k-nearest neighbor and a high-degree polynomial classifier such as multilayer perceptron are employed for classification with all the components and major components case. Macrovascular changes emerged as the principal distinctive factors of ischemic-stroke in diabetes mellitus. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study, based on a small population, supports theories of stroke in diabetes mellitus patients and also concludes that the use of intelligent data analysis improves personalized preventive intervention.

This study proposes a computer-based decision support system to investigate the distinctive factors of diabetes mellitus (DM) with ischemic (non-embolic type) stroke and without stroke. Database consists of a total of 16 features that are collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, higher density lipoprotein (HDL), triglicerit levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease (PVD), myocard infarction (MI) rate, glucose levels, taking medicine, blood pressure. Metric and non-metric features are distinguished. First, the statistics, mean and covariance, of data are estimated and the correlated components are observed. Second, principal component analysis (PCA) is used for major components. Finally, decision making approaches, k-nearest neighbor (k-NN) and MLP, are employed for classification of all the components and major components case. Macrovascular changes emerged as principal distinctive factors of ischemic-stroke in DM. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study supports theories of stroke in DM and also concludes that the use of intelligent data analysis improves personalized prevention.