A review on current advances in machine learning based diabetes prediction

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes.     

Learning using privileged information: SVM+ and weighted SVM

Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time—a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available.     

Automated classification of neonatal sleep states using EEG

Objective

To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age.

Artificial Intelligence in Cardiology

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.     

基于PCA及SVM的运动想象脑电信号识别研究

为了解决脑机接口(BCI)中不同意识任务下运动想象脑电信号的分类问题,提出了一种基于PCA及SVM的识别方法。针对Hilbert-Huang变换和AR模型提取的脑电信号特征,首先采用主成分分析PCA对高维特征向量进行降维处理,然后用支持向量机进行分类。最后将本方法分类结果和Fisher线性分类、概率神经网络分类结果进行比较。实验结果表明,该方法分类正确率较高,复杂度低,具有一定的有效性,可用于脑机接口中。     

基于支持向量机的乳腺癌预后状态预测和疗效评估

乳腺癌是危害妇女健康的主要恶性肿瘤.目前基因与疾病关系的研究取得了一系列的成果,使得利用乳腺癌患者的基因信息来预测预后状态和评估治疗效果成为了可能.支持向量机(support vector machine,SVM)分类方法在实际二类分类问题的应用中显示出良好的学习和泛化能力,已被广泛地应用于诸多研究领域.本文采用支持向量机SVM、K-近邻法(K-nearest neighbor,K-NN)、概率神经网络(probabilistic neural network,PNN)、决策树(decision tree,DT)分类器,结合乳腺癌患者基因数据来预测患者的预后状态和评估治疗效果.结果表明:当使用高斯径向基核函数时,SVM通过5次交叉验证的最佳平均分类准确率达到了88.44%,优于K-NN(81.69%)、PNN(80.68%)和DT(71.19%)等分类器,表明该方法有望成为一种有效、实用的乳腺癌预后状态预测和治疗效果客观评价的工具.     

A learning method for the class imbalance problem with medical data sets

In medical data sets, data are predominately composed of “normal” samples with only a small percentage of “abnormal” ones, leading to the so-called class imbalance problems. In class imbalance problems, inputting all the data into the classifier to build up the learning model will usually lead a learning bias to the majority class. To deal with this, this paper uses a strategy which over-samples the minority class and under-samples the majority one to balance the data sets. For the majority class, this paper builds up the Gaussian type fuzzy membership function and α-cut to reduce the data size; for the minority class, we use the mega-trend diffusion membership function to generate virtual samples for the class.Furthermore, after balancing the data size of classes, this paper extends the data attribute dimension into a higher dimension space using classification related information to enhance the classification accuracy. Two medical data sets, Pima Indians’ diabetes and the BUPA liver disorders, are employed to illustrate the approach presented in this paper. The results indicate that the proposed method has better classification performance than SVM, C4.5 decision tree and two other studies.     

基于SVM的尿液粒子识别算法研究

本文将支持向量机的算法引入到尿沉渣有形成分的分类问题上.在提取特征的基础上,采用交叉验证法和精度等高线图进行核函数及参数的选择.根据支持向量机和数据集特点,设计出由两级分类器集成的支持向量机多分类器.得到了相应的混淆矩阵.临床实验数据分类评测以及与神经网络方法比较结果表明,提出的算法具有一定的优势.