1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

Classification and diagnosis of diabetes

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Coronary artery disease

Evidence-based Complementary and Alternative Medicine简介

A new machine learning technique for an accurate diagnosis of coronary artery disease

An improved noninvasive method to detect Diabetes Mellitus using the Probabilistic Collaborative Representation based Classifier

Multi-view data represented in multiple views contains more complementary information than a single view, whereby multi-view learning explores and utilizes the multi-view data. In general, most existing multi-view learning methods consider the correlation between multiple views. However, the relationship between classes and views which is also important in multi-view learning has never been involved in the existing works. In this paper, we propose a fast and effective multi-view nearest-subspace classifier (MV-NSC) by taking advantage of both the two relationships simultaneously. MV-NSC consists of four main parts: (1) projection residual, (2) view-dependent class separability, (3) view similarity, and (4) final decision. The last part combines the first three parts in one final decision matrix, while the first three parts utilize the information of the multi-view data in various aspects. Our proposed method is evaluated on four benchmark datasets and compared with seven other classifiers including both multi- and single-view algorithms. According to the experimental results, it shows that our proposed method is effective, efficient, and robust in multi-view classification.

/pdf/multi-view-classification-via-a-fast-and-effective-multi-2o86uiy70b.pdf

Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier

At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

/pdf/effective-heart-disease-detection-based-on-quantitative-1pczbso12b.pdf

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers.

Brain disease including any conditions or disabilities that affect the brain is fast becoming a leading cause of death. The traditional diagnostic methods of brain disease are time-consuming, inconvenient and non-patient friendly. As more and more individuals undergo examinations to determine if they suffer from any form of brain disease, developing noninvasive, efficient, and patient friendly detection systems will be beneficial. Therefore, in this paper, we propose a novel noninvasive brain disease detection system based on the analysis of facial colors. The system consists of four components. A facial image is first captured through a specialized sensor, where four facial key blocks are next located automatically from the various facial regions. Color features are extracted from each block to form a feature vector for classification via the Probabilistic Collaborative based Classifier. To thoroughly test the system and its performance, seven facial key block combinations were experimented. The best result was achieved using the second facial key block, where it showed that the Probabilistic Collaborative based Classifier is the most suitable. The overall performance of the proposed system achieves an accuracy −95%, a sensitivity −94.33%, a specificity −95.67%, and an average processing time (for one sample) of <1 min at brain disease detection.

Novel Noninvasive Brain Disease Detection System Using a Facial Image Sensor.

In recent years more and more researchers have done work on Diabetes Mellitus detection through facial image analysis. These methods are non-invasive and convenient to detect Diabetes Mellitus, which are different from the traditional diagnostic methods. In this paper, we propose a new method composed of Gray-scale Histogram Features, Diabetes Mellitus genetic algorithm, and a classifier (k-Nearest Neighbors) with weights to detect Diabetes Mellitus using four facial blocks extracted from the facial image. Firstly, Gray-scale Histogram Features, which represents the distribution of gray-scale values in the image, are extracted from each block to represent it. Next, Diabetes Mellitus genetic algorithm based on genetic algorithm is proposed to select features from the Gray-scale Histogram Features of each block. Finally, k-Nearest Neighbors with weights is used to classify Diabetes Mellitus and Healthy samples. The proposed method is tested on a facial image dataset consisting of 284 Diabetes Mellitus and 142 Healthy samples, which obtains an accuracy of 99.48%.

Using K-NN with weights to detect diabetes mellitus based on genetic algorithm feature selection

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