Performance analysis of classification algorithms on early detection of liver disease

The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area.In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UCI instances of liver disease dataset from the UCI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted C5.0 and CHAID algorithms. Until now there is no work in the literature that uses boosted C5.0 and CHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB = [10.900–1.200], ALB [4.00–4.300], SGPT = [34–37], TB = [0.600–1.200] (by boosted C5.0), A/G = [1.180–1.390], as well as in the Boosted C5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that C5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the CHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted C5.0 and CHAID algorithms are capable to produce rules for liver disease. Our results also show that boosted C5.0 considers the gender in liver disease, a factor which is missing in many other studies. Meanwhile, using the rules generated in boosted C5.0 algorithm, we obtained the important result about low susceptibility of female to liver disease than male. This factor is missing in other studies of liver disease. Therefore, our proposed computer-aided diagnostic methods as an expert and intelligent system have impressive impact on liver disease detection. Based on obtained results, we observed that our model had better performance compared to existing methods in the literature.     

A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inference system

Proper interpretation of the thyroid gland functional data is an important issue in diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by thyroid gland provides this. Production of too little thyroid hormone (hypo-thyroidism) or production of too much thyroid hormone (hyper-thyroidism) defines the types of thyroid disease. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of thyroid disease, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on thyroid disease using principles component analysis (PCA), k-nearest neighbor (k-NN) based weighted pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The proposed system has three stages. In the first stage, dimension of thyroid disease dataset that has 5 features is reduced to 2 features using principles component analysis. In the second stage, a new weighting scheme based on k-nearest neighbor (k-NN) method was utilized as a pre-processing step before the main classifier. Then, in the third stage, we have used adaptive neuro-fuzzy inference system to diagnosis of thyroid disease. We took the thyroid disease dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.     

基于时序卷积网络的早期帕金森多模态检测系统

帕金森病是最常见的神经退行性疾病之一,其临床特征与其他神经退行性疾病有重叠,且缺乏明确的病理机制,导致早期诊断检测困难、误诊率高等问题;为了研究有效的早期帕金森病检测方法,深入探索帕金森病发展的时间特征规律,并提高早期帕金森病预测、分析和诊断决策的准确性,设计了一种基于时序卷积网络的早期帕金森病多模态检测系统,为及时发现早期帕金森病提供辅助诊断依据;该系统利用语音、步态和受试者自测数据,采用多元线性池化方法进行多模态融合,结合时间卷积网络和参数共享方式,以提高系统的检测精度并降低过拟合风险;实验测试结果显示,基于时序卷积网络的早期帕金森病检测系统的准确率达到96.22%,在多项评估指标上优于传统的帕金森检测模型,展现出良好的早期帕金森联合检测效果。     

Auxiliary Diagnosis for Parkinson's Disease Using Multimodal Feature Analysis

Parkinson''s disease is a widespread neurodegenerative disease that slowly impairs motor and certain cognitive skills. It is insidious and incurable, and it causes a significant burden on sufferers and their families. However, clinical diagnosis of Parkinson''s disease typically relies on subjective rating scales, which can be influenced by the examinee''s recall bias and assessor subjectivity. Numerous studies have used diverse methods to investigate the physiological aspects of Parkinson''s disease and have provided objective, quantifiable tools for auxiliary diagnosis. However, given the diversity of neurodegenerative illnesses and the similarities in their effects, it remains a problem among unimodal methodologies built upon the representations of Parkinson''s disease to identify the disease uniquely. To this end, we develop a multimodal diagnostic tool comprising the paradigms that evoke potential Parkinson''s aberrant behaviors. First, parametric tests of the identifying features are performed based on the results of the normal distribution test, and statistically significant feature sets are constructed ($p <$ 0.05). Second, multimodal data are collected from 38 cases in a clinical setting using the MDS-UPDRS scale. Finally, the significance of different feature combinations for the assessment of Parkinson''s disease is analyzed based on gait and eye movement modalities; the high immersion triggered task paradigm and the multimodal Parkinson''s disease diagnostic tool are validated in virtual reality scenarios. It is worth noting that it only take 2--4 tasks for the combination of gait and eye movement modalities to obtain an average AUC of 0.97 and accuracy of 0.92.     

基于MVC三层结构的慢性疾病管理的实现

该慢性疾病管理系统由北京大学第三医院肾内科组织开发,目的有:1)开创一种全新的慢性病管理模型:2)为医患搭建一个高效的疾病管理与交流平台;3)鼓励并促进患者的自我管理;4)提供了一个有利于临床科研工作开展的数据仓库.文章在分析了疾病管理系统的现状的基础上,从系统架构、系统功能、系统设计与实现等方面介绍了一个全新的基于Struts+Hibernate的慢性疾病管理系统.     

An ensemble of k-nearest neighbours algorithm for detection of Parkinson's disease

Parkinson's disease is a disease of the central nervous system that leads to severe difficulties in motor functions. Developing computational tools for recognition of Parkinson's disease at the early stages is very desirable for alleviating the symptoms. In this paper, we developed a discriminative model based on a selected feature subset and applied several classifier algorithms in the context of disease detection. All classifier performances from the point of both stand-alone and rotation-forest ensemble approach were evaluated on a Parkinson's disease data-set according to a blind testing protocol. The new method compared to hitherto methods outperforms the state-of-the-art in terms of both predictions of accuracy (98.46%) and area under receiver operating characteristic curve (0.99) scores applying rotation-forest ensemble k-nearest neighbour classifier algorithm.     

面向血管疾病诊断及预测分析的血流动力学模拟综述

目的血流动力学模拟方法对于揭示动脉瘤、动脉粥样硬化斑块等血管疾病的形成、发展和破裂的病理形成机制、诊断及术后评估具有重要研究意义,已经成为血管疾病诊断与预测分析临床应用与研究领域的一个热点方向。方法根据血流动力学模拟过程中模拟的血流的特征尺度的不同,本文采用宏观尺度血流动力学模拟和多尺度血流动力学模拟的两种分类方法进行综述。结果总结了不同特征尺度模拟情况下的血流动力学模拟与分析方法的国内外研究现状、主要研究方法和关键技术,并阐述了其中存在的研究难点,展望了血流动力学模拟未来的研究发展方向。结论随着当前血管疾病患者人数的日益增长,结合基于图像信息的血管建模技术和人体生理真实性血流信息的多尺度血流动力学模拟方法的研究将成为该领域一个新的研究热点,对于提高我国血管疾病治疗水平具有重要研究意义。     

Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm.     

Containment strategies of epidemic invasions

In this paper, prompted by the practical issue of controlling the Aujeszky disease affecting hogs, we analyse what strategies can be implemented to try and contain an SI epidemic invasion in one and two neighbouring farms. We perform some simulations under different assumptions on the basic system, leading thus to different models. The results show that the disease cannot be arrested completely, since infected remain so for life and the system is of a diffusive type, but its spread can rather be slowed down. The influence of contamination islands, sudden external disease infiltration and containment barriers are simulated. The possible leaking of the infection into a neighbouring farm is also studied. The analysis is then extended to an SIS model allowing disease recovery. Under suitable conditions, harvesting is shown to be an effective containment strategy.     

A non-Markovian SIR network model with fixed infectious period and preventive rewiring

In the face of disease occurrence, susceptible individuals tend to protect themselves by rewiring their links, i.e., cutting off connection with infected person and switching to contact with healthy ones. Therefore, the adaptive rewiring mechanism is considered in the network epidemic model. Moreover, the infection periods for different diseases do not always follow exponential distributions in the process of disease transmission, which may be of fixed length. Using the idea of age-structure, we establish the susceptible–infected–recovered epidemic model for each node and each link, resulting in a delayed non-Markovian SIR pairwise model with fixed infectious period and preventive rewiring, then give the pairwise reproduction number $R_0^p$ and provide the formula for the final epidemic size by rigorous analysis and tedious computation. The simulation results show that adaptive rewiring inhibits the spread of disease and decreases the size of disease outbreak, while the extension of the infectious period promotes disease transmission. In addition, the numerical simulation results are in good agreement with the stochastic simulation. To our best knowledge, the approach in building our model is novel, the results may provide new insights into the study of the network disease transmission.     

The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings

Field spectroscopy is a rapid and non-destructive analytical technique that may be used for assessing plant stress and disease. The objective of this study was to develop spectral indices for detection of Ganoderma disease in oil palm seedlings. The reflectance spectra of oil palm seedlings from three levels of Ganoderma disease severity were acquired using a spectroradiometer. Denoizing and data transformation using first derivative analysis was conducted on the original reflectance spectra. Then, comparative statistical analysis was used to select significant wavelength from transformed data. Wavelength pairs of spectral indices were selected using optimum index factor. The spectral indices were produced using the wavelength ratios and a modified simple ratio method. The relationship analysis between spectral indices and total leaf chlorophyll (TLC) was conducted using regression technique. The results suggested that six spectral indices are suitable for the early detection of Ganoderma disease in oil palm seedlings. Final results after regression with TLC showed that Ratio 3 is the best spectral index for the early detection of Ganoderma infection in oil palm seedlings. For future works, this can be used for the development of robust spectral indices for Ganoderma disease detection in young and mature oil palm using airborne hyperspectral imaging.     

Plant disease leaf image segmentation based on superpixel clustering and EM algorithm

Plant disease leaf image segmentation plays an important role in the plant disease detection through leaf symptoms. A novel segmentation method of plant disease leaf image is proposed based on a hybrid clustering. The whole color leaf image is firstly divided into a number of compact and nearly uniform superpixels by superpixel clustering, which can provide useful clustering cues to guide image segmentation to accelerate the convergence speed of the expectation maximization (EM) algorithm, and then, the lesion pixels are quickly and accurately segmented from each superpixel by EM algorithm. The experimental results and the comparison results with similar approaches demonstrate that the proposed method is effective and has high practical value for plant disease detection.

     

Estimation of the disease-specific diagnostic marker distribution under verification bias

We consider the estimation of the parameters indexing a parametric model for the conditional distribution of a diagnostic marker given covariates and disease status. Such models are useful for the evaluation of whether and to what extent a marker’s ability to accurately detect or discard disease depends on patient characteristics. A frequent problem that complicates the estimation of the model parameters is that estimation must be conducted from observational studies. Often, in such studies not all patients undergo the gold standard assessment of disease. Furthermore, the decision as to whether a patient undergoes verification is not controlled by study design. In such scenarios, maximum likelihood estimators based on subjects with observed disease status are generally biased. In this paper, we propose estimators for the model parameters that adjust for selection to verification that may depend on measured patient characteristics and additionally adjust for an assumed degree of residual association. Such estimators may be used as part of a sensitivity analysis for plausible degrees of residual association. We describe a doubly robust estimator that has the attractive feature of being consistent if either a model for the probability of selection to verification or a model for the probability of disease among the verified subjects (but not necessarily both) is correct.     

A thematic analysis of patient communication in Parkinson’s disease online support group discussion forums

Online support groups have become a popular source of information, advice and support for individuals living with a range of health conditions. However, research has not commonly focused on patients living with Parkinson’s disease and their use of online support groups. Thus, the aim of this study was to gain an insight into the positive and negative aspects of online communication through an analysis of messages exchanged within Parkinson’s disease discussion forums. Data was collected from four forums and analysed using data-driven thematic analysis. The results revealed that participation in the forums allowed patients to share experiences and knowledge, form friendships, as well as helping them cope with the challenges of living with Parkinson’s disease. Conversely, a lack of replies, the experience of Parkinson’s disease symptoms, a lack of personal information, fragility of online relationships, misunderstandings and disagreements, all appeared to compromise the online experience. Practical implications and future research recommendations are proposed.     

智能导医系统中TF-IDF权重改进算法研究

面向患者的智能导医系统通过人工智能技术,依据患者症状计算可能疾病,引导患者准确挂号。目前智能导医系统多采用患者输入描述自身症状或者提问的方式,该方式易出现患者输入与医学专业症状词不匹配的问题,导致计算出的疾病可信度较低。针对这一问题,提出重心后移和医学专业语料库相结合的方法,对同义词匹配,映射出与患者症状对应的症状词;根据症状不论重要与否在每一疾病中仅出现一次的特点,提出基于患者关注度的症状词频计算方法;针对传统TF-IDF算法在待分类疾病类中数量分布不均时提取疾病效果差的问题,提出基于疾病类间分布的症状权重改进算法。实验结果表明,改进算法在疾病推荐正确率和可信度两方面具有更好的效果。     

Analysing the influence of African dust storms on the prevalence of coral disease in the Caribbean Sea using remote sensing and association rule data mining

The application of an association rule data mining algorithm is described to combine remote sensing and in-situ geophysical data to show a relationship between African dust storms, Caribbean climate, and Caribbean coral disease. An analysis is performed to quantify the relative statistical significance of each Caribbean climate parameter on the prevalence of coral disease. Results show that African dust storms contribute to an increased prevalence of coral disease in the Caribbean Sea, and that the correlation between them is influenced by other climate parameters, especially sea surface temperature.     

The power of linkage analysis of a disease-related endophenotype using asymmetrically ascertained sib pairs

A linkage study of a qualitative disease endophenotype in a sample of sib pairs, consisting of one disease affected proband and one sibling is considered. The linkage statistic compares marker allele sharing with the proband in siblings with an abnormal endophenotype to siblings with the normal endophenotype. Expressions are derived for the distribution of this linkage statistic, in terms of the recombination fraction and (1) the genetic parameter values (allele frequency and endophenotype and disease penetrance) and (2) the abnormal endophenotype rates in the population and in classes of relatives of disease affected probands. It is then shown that when either the disease or the abnormal endophenotype has additive penetrance, the expressions simplify to a monotonic function of the difference between abnormal endophenotype rates in siblings and in the population. Thought disorder is considered as a putative schizophrenia endophenotype. Forty sets of genetic parameter values that correspond to the known prevalence values for thought disorder in schizophrenic patients, siblings of schizophrenics and the general population are evaluated. For these genetic parameter values, numerical results show that the test statistic has >70% power (α=0.0001) in general with a sample of 200 or more proband-sibling pairs to detect linkage between a marker (θ=0.01) and a locus pleiotropic for schizophrenia and thought disorder.     

基于改进的有限马尔可夫链的桥梁性能退化预测系统

桥梁是交通系统的重要组成部分,对在役桥梁的结构性能进行有效评估和预测能促进桥梁的养护工作。根据实际项目应用情况,提出了一种符合国家行业标准,有效地进行桥梁结构性能评估与退化预测的模型。该模型建立了基于病害影响因素、病害、构件、桥梁与项目的自下向上五个层次的基于优化的有限马尔可夫链的桥梁结构性能退化预测方法,通过实际检测数据说明了该模型的适用性。最后对桥梁检测中得到的所有病害种类进行统计分析,结合病理分析知识库和改进的有限马尔可夫链,发现了现有桥梁的主要病害模式和主要病害因素,并预测桥梁未来的主要病害模式和主要病害因素。     

The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases

This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.     

A Multicriteria Weighted Vote‐Based Classifier Ensemble for Heart Disease Prediction

The availability of a large amount of medical data leads to the need of intelligent disease prediction and analysis tools to extract hidden information. A large number of data mining and statistical analysis tools are used for disease prediction. Single data‐mining techniques show acceptable level of accuracy for heart disease diagnosis. This article focuses on prediction and analysis of heart disease using weighted vote‐based classifier ensemble technique. The proposed ensemble model overcomes the limitations of conventional data‐mining techniques by employing the ensemble of five heterogeneous classifiers: naive Bayes, decision tree based on Gini index, decision tree based on information gain, instance‐based learner, and support vector machines. We have used five benchmark heart disease data sets taken from UCI repository. Each data set contains different set of feature space that ultimately leads to the prediction of heart disease. The effectiveness of proposed ensemble classifier is investigated by comparing the performance with different researchers' techniques. Tenfold cross‐validation is used to handle the class imbalance problem. Moreover, confusion matrices and analysis of variance statistics are used to show the prediction results of all classifiers. The experimental results verify that the proposed ensemble classifier can deal with all types of attributes and it has achieved the high diagnosis accuracy of 87.37%, sensitivity of 93.75%, specificity of 92.86%, and F‐measure of 82.17%. The F‐ratio higher than the F‐critical and p‐value less than 0.01 for a 95% confidence interval indicate that the results are statistically significant for all the data sets.