Improving the efficiency of classification of gastrointestinal disease phenotypes using data processing methods

Automation in arrhythmia classification helps medical professionals to make accurate decisions upon the patient's health. Classification becomes complicated when class overlapping and class imbalance problem occurs together. The aim of this work is to improve the arrhythmia classification accuracy. Proposed methodology consists of fisher discriminant ratio based feature ranking stage and anomaly detection based training sample selection stage followed by classification using probabilistic neural network classifier. As per the recommendations of the Association for the Advancement of Medical Instrumentation, five arrhythmia classes were classified. The proposed method resulted in average sensitivity, positive predictive value and F Score of 95.37%, 98.35% and 96.72%, respectively. The experimental results revealed that: (1) Selected non-overlapping features were able to better discriminate arrhythmia classes, (2) Mixture of Gaussians based anomaly detection method suited well to handle the class imbalance problem and (3) Minority classes with few training samples were also correctly classified using the proposed method.

A machine learning approach for hierarchical classification of software requirements

Approaches to imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of hidden structure within the majority class. The aim of this paper is to highlight the effect of sub-clusters within the majority class on detecting minority class instances, and handle imbalanced classification by learning the structure in the data. We propose a decomposition based approach to two-class imbalanced classification problem. This approach works by first learning the hidden structure of the majority class using an unsupervised learning algorithm. Thus, transforming the classification problem into several classification sub-problems. The base classifier is constructed on each sub-problem. The ensemble is tuned to increase its sensitivity towards minority class. The proposed approach overcomes the limitations of conventional classifiers on imbalanced problem, and combats imbalance by learning hidden structure in the majority class, which is neglected in most existing works. We demonstrate the performance of the proposed approach on several datasets.

Non-overlapping classification of hyperspectral imagery with superpixel segmentation

An In-hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting in China

In this study we use aggregated weighted scores of environmental effects to study environmental influences on well-being and happiness. To this end, we split a sample of Netherlands Twin Register (NTR) participants into a training (N = 4857) and test (N = 2077) sample. In the training sample, we use elastic net regression to estimate effect sizes for associations between life satisfaction and two sets of environmental variables: one based on self-report socioenvironmental data, and one based on objective physical environmental data. Based on these effect sizes, we create two poly-environmental scores (PES-S and PES-O, for self-reports and objective data respectively). In the test sample, we perform association analyses between different measures of well-being and the two PESs. We find that the PES-S explains ∼36% of the variance in well-being, while the PES-O does not significantly contribute to the model. Variance in other well-being measures (i.e., different life satisfaction domains, subjective happiness, quality of life, flourishing, psychological well-being, self-rated health, depressive problems, and loneliness) are explained to varying extents by the PESs, ranging from 6.36% (self-rated health) to 36.66% (loneliness). These predictive values did not change during the COVID-19 pandemic (N = 3214). Validating the PES-S in the UK biobank (N = 40,614), we find that the UK biobank PES-S explains about ∼12% of the variance in happiness. Lastly, we examine if there is any indication for gene-environment correlation (rGE), the phenomenon where one's genetic predisposition influences exposure to the environment, by associating the PESs with polygenic scores (PGS) in a sample of Netherlands Twin Register (NTR) and UK Biobank participants. While the PES and PGS were not correlated in the NTR sample, they were correlated in the larger UK biobank sample, indicating the potential presence of rGE. We discuss several limitations pertaining to our dataset, such as a potential influence of common method bias, and reflect on how PESs might be used in future research.

Objective To investigate the feasibility of speech recognition technologies including double threshold method, Mel frequency cepstral coefficient (MFCC) and vector quantization (VQ) in automatic recognition of cough sounds. Methods Five healthy adults and 15 patients with cough were recruited for recording of both non- cough and cough sounds in a quiet environment. The records were randomized into training and testing samples. The training samples were used to generate the code book for cough recognition software, which was then used to recognize and analyze the testing samples automatically.The sensitivity and specificity of recognition were calculated by comparison with outcomes from human ear recognition. The recognition time in two approaches was recorded. Results Two hundred cough and 200 noncough sound samples were used to generate the code book for cough recognition software, while 375 cough sound samples and 125 non-cough sound samples were used as the testing samples. The recognition time of the testing samples needed was 33 minutes and 18 seconds by human ear recognition vs 1 minute and 35seconds by code book-based automatic recognition. In addition, the sensitivity and specificity in code bookbased automatic recognition of the cough sound were 98.93% and 100% respectively. Conclusion The double threshold method based on VQ and MFCC appears feasible in automatic recognition of cough sounds. Key words: Cough; Speech recognition software; Pattern recognition, automatic; Sensitivity and specificity; Human ear recognition

This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

Layer-by-layer description of the core is performed to understand the regularities of the structure of the geological section, predict the development of reservoirs, clarify stratigraphic boundaries and obtain calculation parameters for assessing hydrocarbon reserves. In this case, the name of the breed is one of the key parameters determined in the layer-by-layer description. This paper presents a comparative analysis of two approaches to determining the breed using machine learning methods: based on graphical identifiers and convolutional neural networks. The original sample contained photographs of core samples from the Tyumenskaya suite fields (8 fields, 15 wells, more than 2 km of core) under daylight. For the analysis, 4 main classes of rocks (siltstones, mudstones, sandstones, coals) were selected. For these rocks, windows of 5 × 5 cm were formed and compressed to 299 × 299 pixels. The total sample exceeded 90,000 windows: 70% — training sample (60,359 windows) and 30% — test (31,140 windows). The training and test samples contain photographs of core samples from different fields. The comparison was made between convolutional neural networks (ResNet, ResNeXt, Inception, etc.) and a classifier (such as XGBoost) based on graphic identifiers of two types: color (average color, dominant colors) and texture (entropy, Euler’s number, contrast, dissimilarity, uniformity, energy, correlation). According to the results of the experiments, the model based on convolutional neural networks turned out to be more sensitive to implicit features and made it possible to reduce the error in the weighted average f1-measure with respect to the ensemble of weak classifiers by 12.5% on the test sample even without optimization of hyperparameters. Thus, we can conclude that the model based on convolutional neural networks is more sensitive to implicit features that are difficult to extract using known graphic identifiers. On the other hand, the approach based on graphic identifiers and an ensemble of weak classifiers can be used without specialized computing power (video cards).

Class imbalance problem is prevalent in many real-world domains. It has become an active area of research. In binary classification problems, imbalance learning refers to learning from a dataset with a high degree of skewness to the negative class. This phenomenon causes classification algorithms to perform woefully when predicting positive classes with new examples. Data resampling, which involves manipulating the training data before applying standard classification techniques, is among the most commonly used techniques to deal with the class imbalance problem. This article presents a new hybrid sampling technique that improves the overall performance of classification algorithms for solving the class imbalance problem significantly. The proposed method called the Hybrid Cluster-Based Undersampling Technique (HCBST) uses a combination of the cluster undersampling technique to under-sample the majority instances and an oversampling technique derived from Sigma Nearest Oversampling based on Convex Combination, to oversample the minority instances to solve the class imbalance problem with a high degree of accuracy and reliability. The performance of the proposed algorithm was tested using 11 datasets from the National Aeronautics and Space Administration Metric Data Program data repository and University of California Irvine Machine Learning data repository with varying degrees of imbalance. Results were compared with classification algorithms such as the K-nearest neighbours, support vector machines, decision tree, random forest, neural network, AdaBoost, naïve Bayes, and quadratic discriminant analysis. Tests results revealed that for the same datasets, the HCBST performed better with average performances of 0.73, 0.67, and 0.35 in terms of performance measures of area under curve, geometric mean, and Matthews Correlation Coefficient, respectively, across all the classifiers used for this study. The HCBST has the potential of improving the performance of the class imbalance problem, which by extension, will improve on the various applications that rely on the concept for a solution.

The accuracy of the data class is very important in classification with a machine learning approach. The more accurate the existing data sets and classes, the better the output generated by machine learning. In fact, classification can experience imbalance class data in which each class does not have the same portion of the data set it has. The existence of data imbalance will affect the classification accuracy. One of the easiest ways to correct imbalanced data classes is to balance it. This study aims to explore the problem of data class imbalance in the medium case dataset and to address the imbalance of data classes as well. The Synthetic Minority Over-Sampling Technique (SMOTE) method is used to overcome the problem of class imbalance in obesity status in Indonesia 2013 Basic Health Research (RISKESDAS). The results show that the number of obese class (13.9%) and non-obese class (84.6%). This means that there is an imbalance in the data class with moderate criteria. Moreover, SMOTE with over-sampling 600% can improve the level of minor classes (obesity). As consequence, the classes of obesity status balanced. Therefore, SMOTE technique was better compared to without SMOTE in exploring the obesity status of Indonesia RISKESDAS 2013.

Individualized boosting learning for classification

Problems of Class Imbalance in data classification have received attention from many researchers. It is because the imbalance class will affect the accuracy of the classification results. The problem of the imbalance class itself will ignore the minority class, which is a class with a smaller number of instances even though the minority class is an exciting class to observe. In overcoming the imbalanced class problem, it is necessary to pay attention to diversity data, the number of classifiers, and also classification performance. Several methods have been proposed to overcome the imbalanced class problem, one of which is the Hybrid Approach Redefinition Method. This method is a good hybrid ensemble method in dealing with imbalance class problems, which can provide useful diversity data and also a smaller number of classifiers. This research will combine the Hybrid Approach Redefinition by replacing the use of SMOTE Boost by using Progressive Boosting to get better data diversity, a small number of classifiers, and better performance. This study will conduct testing in handling imbalance class problems using datasets sourced from the KEEL-Dataset Repository. The results of this study indicate that the Hybrid Approach Redefinition with Progressive Boosting will provide better results in the number of classifiers, data diversity, and classification performance.

The class imbalance problem is encountered in real-world applications of machine learning and results in a classifier's suboptimal performance. Researchers have rigorously studied the resampling, algorithms, and feature selection approaches to this problem. No systematic studies have been conducted to understand how well these methods combat the class imbalance problem and which of these methods best manage the different challenges posed by imbalanced data sets. In particular, feature selection has rarely been studied outside of text classification problems. Additionally, no studies have looked at the additional problem of learning from small samples. This paper presents a first systematic comparison of the three types of methods developed for imbalanced data classification problems and of seven feature selection metrics evaluated on small sample data sets from different applications. We evaluated the performance of these metrics using area under the receiver operating characteristic (AUC) and area under the precision-recall curve (PRC). We compared each metric on the average performance across all problems and on the likelihood of a metric yielding the best performance on a specific problem. We examined the performance of these metrics inside each problem domain. Finally, we evaluated the efficacy of these metrics to see which perform best across algorithms. Our results showed that signal-to-noise correlation coefficient (S2N) and Feature Assessment by Sliding Thresholds (FAST) are great candidates for feature selection in most applications, especially when selecting very small numbers of features.

Detecting cracks on concrete surface images is a key inspection for maintaining infrastructures such as bridge and tunnels. From the viewpoint of computer vision, the task of automatic crack detection poses two challenges. First, since the cracks are visually depicted by subtle patterns and also exhibit similar appearance to the other structural patterns, it is difficult to discriminatively characterize such less distinctive and finer defects. Second, the cracks are scarcely found, making the number of training samples for cracks significantly smaller than that of the other normal samples to be distinguished from the cracks. This is regarded as a class imbalance problem where the classifier is highly biased toward majority classes. In this study, we propose two methods to address these issues in the framework of deep learning for crack detection: a novel network, called Spiral-Net, and an effective optimization method to train the network. The proposed network is extended from U-Net to extract more detailed visual features, and the optimization method is formulated based on F1 score (F-measure) for properly learning the network even on the highly imbalanced training samples. The experimental results on crack detection demonstrate that the two proposed methods contribute to performance improvement individually and jointly.