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Support vector machine

Jorge Barros, Susana Morales, Orietta Echávarri, Arnol García, Jaime Ortega, Takeshi Asahi, Claudia Moya, Ronit Fischman, María P Maino, Catalina Núñez
Objective: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. Methods: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. Results: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0...
October 20, 2016: Revista Brasileira de Psiquiatria
Wei Liu, Changhong Liu, Feng Chen, Jianbo Yang, Lei Zheng
Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype...
October 26, 2016: Scientific Reports
Deepak Ranjan Nayak, Ratnakar Dash, Banshidhar Majhi
This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed images. The feature vector is constructed using the energy and entropy values, computed from the level-2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty ranking filter...
October 24, 2016: CNS & Neurological Disorders Drug Targets
J S Yu, A Y Xue, E E Redei, N Bagheri
Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A notable barrier in addressing this public health threat involves reliable identification of the disorder, as many affected individuals remain undiagnosed or misdiagnosed. An objective blood-based diagnostic test using transcript levels of a panel of markers would provide an invaluable tool for MDD as the infrastructure-including equipment, trained personnel, billing, and governmental approval-for similar tests is well established in clinics worldwide...
October 25, 2016: Translational Psychiatry
Hamidreza Kavianpour, Mahdi Vasighi
Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods...
October 24, 2016: Amino Acids
Cheng Wang, Hongqian Chen, Xuebin Zhang, Chaoying Meng
BACKGROUND: Behavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts. There is an urgent need for automatic identification of individual animals and automatic tracking is a fundamental part of the solution to this problem. RESULTS: In this study, an algorithm based on a Hybrid Support Vector Machine (HSVM) was developed for the automated tracking of individual laying hens in a layer group...
2016: Journal of Animal Science and Biotechnology
Qikang Wei, Tao Chen, Ruifeng Xu, Yulan He, Lin Gui
The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization...
2016: Database: the Journal of Biological Databases and Curation
Raminta Daniulaityte, Lu Chen, Francois R Lamy, Robert G Carlson, Krishnaprasad Thirunarayan, Amit Sheth
BACKGROUND: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. OBJECTIVES: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets...
October 24, 2016: JMIR Public Health and Surveillance
Laila Khedher, Ignacio A Illán, Juan M Górriz, Javier Ramírez, Abdelbasset Brahim, Anke Meyer-Baese
Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making...
July 22, 2016: International Journal of Neural Systems
Duy Dao, S M A Salehizadeh, Yeon Noj, Jo Woon Chong, Chae Cho, Dave Mcmanus, Chad E Darling, Yitzhak Mendelson, Ki H Chon
Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study we present a novel approach, "TifMA," based on using the Time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the non-usable part of the corrupted data. The term "non-usable" refers to segments of PPG data from which the HR signal cannot be recovered accurately...
October 21, 2016: IEEE Journal of Biomedical and Health Informatics
Hongqiang Li, Danyang Yuan, Youxi Wang, Dianyin Cui, Lu Cao
Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features...
October 20, 2016: Sensors
Dongrae Cho, Beomjun Min, Jongin Kim, Boreom Lee
In this study, we examined the phase locking value (PLV) for seizure prediction, particularly, in the gamma frequency band. We prepared simulation data and 65 clinical cases of seizure. In addition, various filtering algorithms including bandpass filtering, empirical mode decomposition, multivariate empirical mode decomposition and noise-assisted multivariate empirical mode decomposition (NA-MEMD) were used to decompose spectral components from the data. Moreover, in the case of clinical data, the PLVs were used to classify between interictal and preictal stages using a support vector machine...
October 19, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Panagiotis Tsakanikas, Dimitris Pavlidis, Efstathios Panagou, George-John Nychas
Recently, imaging and machine vision are gaining attention to food stakeholders since these are considered to be the emerging tools for food safety and quality assessment throughout the whole food chain. Herein, multispectral imaging, a surface chemistry sensor type, has been evaluated in terms of monitoring aerobically packaged beef filet spoilage at different storage temperatures (2, 8, and 15°C) and storage time. Spectral data acquired from the surface of meat samples (with/without background flora; +BF/-BF respectively) along with microbiological analysis...
December 1, 2016: Talanta
Zhiwei Zhou, Xiaotao Shen, Jia Tu, Zheng-Jiang Zhu
The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility - mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites...
October 21, 2016: Analytical Chemistry
Yu Jiang, Changying Li, Fumiomi Takeda
Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation...
October 21, 2016: Scientific Reports
Mutlu Mete, Unal Sakoglu, Jeffrey S Spence, Michael D Devous, Thomas S Harris, Bryon Adinoff
BACKGROUND: Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented...
October 6, 2016: BMC Bioinformatics
G Guidi, N Maffei, B Meduri, E D'Angelo, G M Mistretta, P Ceroni, A Ciarmatori, A Bernabei, S Maggi, M Cardinali, V E Morabito, F Rosica, S Malara, A Savini, G Orlandi, C D'Ugo, F Bunkheila, M Bono, S Lappi, C Blasi, F Lohr, T Costi
PURPOSE: To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset. MATERIALS AND METHODS: 90 patients (pts) treated for head-neck cancer (H&N) formed a multicenter data-set. 41 H&N pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG)...
October 17, 2016: Physica Medica: PM
Jingting Xu, Hong Hu, Yang Dai
[This corrects the article DOI: 10.1371/journal.pone.0163491.].
2016: PloS One
Yingwang Gao, Jinfeng Geng, Xiuqin Rao, Yibin Ying
Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests)...
October 18, 2016: Sensors
Hasseeb Azzawi, Jingyu Hou, Yong Xiang, Russul Alanni
Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models...
October 2016: IET Systems Biology
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