SEARCH · PubMed Health

Results for “Classification Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,171 records · Page 65Linked to original sources

PubMed2005

A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control.

This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.

Adult↗

PubMed2005

Development of a kinetic analysis technique for PACS management and a screening examination in dynamic radiography.

The purpose of this study was to develop a method of kinetic analysis for picture archiving and communication system (PACS) management and computer-aided diagnostic application in dynamic chest radiography. The main analytical technique used in this study was a new algorithm that converts dynamic radiographs into a color-static image. The algorithm is a visualization technique for kinetic information that uses the intensity-density transformation and the direction classification in optical flow. The image made by the new algorithm was defined as a "kinetic map," and, by analysis using the kinetic map, a patient collation system and nodule detection system were constructed. By analysis that used an artificial neural network of certain feature vectors as kinetic map similarity, the collation system obtained good identification performance. Temporal subtraction processing between a current-status map with simulated nodule and previous-status map detected the region of abnormality as the simulated nodule. It is expected that our method of analysis will be useful as a screening examination for risk management and computer-aided diagnostic application in dynamic chest radiography.

Adult↗

PubMedDate not supplied

[Assessment of the degree of severity of radiation lesions in dogs using densitometric-geometric parameters of the blood lymphocytes].

Changes in densitometric-geometric parameters of lymphocyte nuclei of dog peripheral blood detected at early times (6 and 24 H) following gamma irradiation with 2.5 and 6.0 Gy are given a composite description. Using densitometric-geometric parameters the authors have developed an algorithm allowing for a 90% accuracy in the classification of objects according to the severity of radiation affection.

Algorithms↗

PubMed2005

Automated classification and analysis of the calcium response of single T lymphocytes using a neural network approach.

The gene activities in T lymphocytes that regulate immune responses are influenced by Ca2+ ([Ca2+]i). The intracellular calcium signals are highly heterogeneous and vitally important in determining the immune outcome. The signals in individual cells can be measured using fluorescence microscopy but to group the cells into classes with similar signal kinetics is currently laborious. Here, we demonstrate a method for the automated classification of the responses into four categories formerly identified by an expert's inspection. This method comprises characterising the response by a second-order model, performing frequency analysis, and using derived features as inputs to two multilayer perceptron neural networks (NNs). We compare the algorithm's performance on an example data set against the human classification: it was found to classify identically more than 70% of the data, despite small sample sizes in two categories and significant overlap between the other two classes. The group characterized by an oscillating signal showed the presence of a number of frequencies, which may be important in determining gene activation. A classification threshold enables the automatic identification of patterns with a low-classification certainty. Future refinement of the algorithm may allow the identification of more classes, which may be important in different immune responses associated with disease.

Algorithms↗

PubMed2000

Novel approach to acoustical voice analysis using artificial neural networks.

Perceptual rating scales are widely used for the assessment of voice quality. These ratings may be influenced by the individual experience of the listener. Thus, researchers have turned to acoustical measures which may eventually correlate with voice quality. In this study we tested whether multivariate statistics, combined with artificial neural networks, could identify patterns of acoustic voice parameters corresponding to a widely used perceptual rating scale. In a multicenter study with 31 raters, voice samples of 117 individuals with or without voice disorders were perceptually rated. The RBH index, consisting of a 4-point scale of roughness, breathiness, and hoarseness, was used. Voice samples were then analyzed with an acoustical feature extraction and classified using amultivariate regression tree analysis with the perceptual ratings as a priori information. Artificial neural networks were trained to selected acoustic parameters having high "relative importance" in the regression trees. Mean classification accuracies were around 30% with topographic feature maps (trained with Learning Vector Quantization algorithm) and 65-85% with feedforward networks (trained with RProp algorithm). Based on the best-fitting results with feedforward networks, a classification system (computer program) consisting of 50 simultaneous working networks was developed. Using this program, the classification matched 40% of the a prori values in both R and B domains. In 65% they matched at least in one domain. These accuracies are within the range reported by other authors using artificial neural networks in biology and clinical medicine. Thus, the results encourage further research of feedforward networks for acoustic voice analysis.

Humans↗

PubMed1999

An adaptive segmentation algorithm for time-of-flight MRA data.

A three-dimensional (3-D) representation of cerebral vessel morphology is essential for neuroradiologists treating cerebral aneurysms. However, current imaging techniques cannot provide such a representation. Slices of MR angiography (MRA) data can only give two-dimensional (2-D) descriptions and ambiguities of aneurysm position and size arising in X-ray projection images can often be intractable. To overcome these problems, we have established a new automatic statistically based algorithm for extracting the 3-D vessel information from time-of-flight (TOF) MRA data. We introduce distributions for the data, motivated by a physical model of blood flow, that are used in a modified version of the expectation maximization (EM) algorithm. The estimated model parameters are then used to classify statistically the voxels into vessel or other brain tissue classes. The algorithm is adaptive because the model fitting is performed recursively so that classifications are made on local subvolumes of data. We present results from applying our algorithm to several real data sets that contain both artery and aneurysm structures of various sizes.

Algorithms↗

PubMed2001

A fast and convergent stochastic MLP learning algorithm.

We propose a stochastic learning algorithm for multilayer perceptrons of linear-threshold function units, which theoretically converges with probability one and experimentally exhibits 100% convergence rate and remarkable speed on parity and classification problems with typical generalization accuracy. For learning the n bit parity function with n hidden units, the algorithm converged on all the trials we tested (n=2 to 12) after 5.8 x 4.1(n) presentations for 0.23 x 4.0(n-6) seconds on a 533MHz Alpha 21164A chip on average, which is five to ten times faster than Levenberg-Marquardt algorithm with restarts. For a medium size classification problem known as Thyroid in UCI repository, the algorithm is faster in speed and comparative in generalization accuracy than the standard backpropagation and Levenberg-Marquardt algorithms.

Algorithms↗

PubMed2004

Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation.

It is well known in the pattern recognition community that the accuracy of classifications obtained by combining decisions made by independent classifiers can be substantially higher than the accuracy of the individual classifiers. We have previously shown this to be true for atlas-based segmentation of biomedical images. The conventional method for combining individual classifiers weights each classifier equally (vote or sum rule fusion). In this paper, we propose two methods that estimate the performances of the individual classifiers and combine the individual classifiers by weighting them according to their estimated performance. The two methods are multiclass extensions of an expectation-maximization (EM) algorithm for ground truth estimation of binary classification based on decisions of multiple experts (Warfield et al., 2004). The first method performs parameter estimation independently for each class with a subsequent integration step. The second method considers all classes simultaneously. We demonstrate the efficacy of these performance-based fusion methods by applying them to atlas-based segmentations of three-dimensional confocal microscopy images of bee brains. In atlas-based image segmentation, multiple classifiers arise naturally by applying different registration methods to the same atlas, or the same registration method to different atlases, or both. We perform a validation study designed to quantify the success of classifier combination methods in atlas-based segmentation. By applying random deformations, a given ground truth atlas is transformed into multiple segmentations that could result from imperfect registrations of an image to multiple atlas images. In a second evaluation study, multiple actual atlas-based segmentations are combined and their accuracies computed by comparing them to a manual segmentation. We demonstrate in both evaluation studies that segmentations produced by combining multiple individual registration-based segmentations are more accurate for the two classifier fusion methods we propose, which weight the individual classifiers according to their EM-based performance estimates, than for simple sum rule fusion, which weights each classifier equally.

Algorithms↗

PubMed2006

Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules.

MOTIVATION: Microarrays datasets frequently contain a large number of missing values (MVs), which need to be estimated and replaced for subsequent data mining. The focus of the paper is to study the effects of different MV treatments for cDNA microarray data on disease classification analysis. RESULTS: By analyzing five datasets, we demonstrate that among three kinds of classifiers evaluated in this study, support vector machine (SVM) classifiers are robust to varied MV imputation methods [e.g. replacing MVs by zero, K nearest-neighbor (KNN) imputation algorithm, local least square imputation and Bayesian principal component analysis], while the classification and regression tree classifiers are sensitive in terms of classification accuracy. The KNNclassifiers built on differentially expressed genes (DEGs) are robust to the varied MV treatments, but the performances of the KNN classifiers based on all measured genes can be significantly deteriorated when imputing MVs for genes with larger missing rate (MR) (e.g. MR > 5%). Generally, while replacing MVs by zero performs relatively poor, the other imputation algorithms have little difference in affecting classification performances of the SVM or KNN classifiers. We further demonstrate the power and feasibility of our recently proposed functional expression profile (FEP) approach as means to handle microarray data with MVs. The FEPs, which are derived from the functional modules that are enriched with sets of DEGs and thus can be consistently identified under varied MV treatments, achieve precise disease classification with better biological interpretation. We conclude that the choice of MV treatments should be determined in context of the later approaches used for disease classification. The suggested exclusion criterion of ignoring the genes with larger MR (e.g. >5%), while justifiable for some classifiers such as KNN classifiers, might not be considered as a general rule for all classifiers.

Algorithms↗

PubMed1999

Markov random field modeling in posteroanterior chest radiograph segmentation.

Previously, the authors presented an algorithm that identifies lung regions in a digitized posteroanterior chest radiograph (DCR) by labeling each pixel as either lung or nonlung. In this manuscript, the inherent flexibility of this algorithm is demonstrated as the algorithm is generalized to identify multiple anatomical regions in a DCR. Specifically, each pixel is classified as belonging to one of six anatomical region types: lung, subdiaphragm, heart, mediastinum, body, or background. The algorithm determines the optimal set of pixel classifications, xOPT, for a given set of DCR pixel gray level values y via a probabilistic approach that defines xOPT as the particular segmentation that maximizes the conditional distribution P(x/y). A spatially varying Markov random field (MRF) model is used that incorporates spatial and textural information of each possible region type. MRF modeling provides the form of P(x/y), and Iterated Conditional Modes is used to converge to the distribution maximum of P(x/y) thus obtaining the optimal segmentation for a given DCR. Results show the algorithm being able to correctly classify 90.0% +/- 3.4% of the pixels in a DCR.

Algorithms↗

PubMed1999

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification.

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.

Algorithms↗

PubMed2003

Predicting gene function in Saccharomyces cerevisiae.

MOTIVATION: S.cerevisiae is one of the most important model organisms, and has has been the focus of over a century of study. In spite of these efforts, 40% of its open reading frames (ORFs) remain classified as having unknown function (MIPS: Munich Information Center for Protein Sequences). We wished to make predictions for the function of these ORFs using data mining, as we have previously successfully done for the genomes of M.tuberculosis and E.coli. Applying this approach to the larger and eukaryotic S.cerevisiae genome involves modifying the machine learning and data mining algorithms, as this is a larger organism with more data available, and a more challenging functional classification. RESULTS: Novel extensions to the machine learning and data mining algorithms have been devised in order to deal with the challenges. Accurate rules have been learned and predictions have been made for many of the ORFs whose function is currently unknown. The rules are informative, agree with known biology and allow for scientific discovery. AVAILABILITY: All predictions are freely available from http://www.genepredictions.org, all datasets used in this study are freely available from http://www.aber.ac.uk/compsci/Research/bio/dss/yeastdataand software for relational data mining is available from http://www.aber.ac.uk/compsci/Research/bio/dss/polyfarm.

Chromosome Mapping↗

PubMed2005

Advanced soft computing diagnosis method for tumour grading.

OBJECTIVE: To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied. MATERIAL AND METHODS: One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM. RESULTS: The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively. CONCLUSIONS: An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.

Algorithms↗

PubMed1979

A national study of medical and surgical specialties. III. An empirical approach to the classification of patient care.

A major feature of a national survey of medical and surgical specialties is the development and application of an algorithm for classifying patient care services provided by physicians. The care classification reflects much of prevailing opinion regarding what constitutes primary and nonprimary care. The classification system provides a powerful tool for the analysis of patient care services, since it is based on conditions of access to care, the physician's role in providing the care, measures associated with continuity of care, and a proxy measure of comprehensiveness of care. Furthermore, it is based on the recordings by physicians of actual patient-encounter characteristics and is not operationally dependent on physician characteristics or propensities.

Cardiology↗

PubMed2001

Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm.

In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fisher's Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

Algorithms↗

PubMed2025

Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

Humans↗

PubMed2005

Application of compression-based distance measures to protein sequence classification: a methodological study.

MOTIVATION: Distance measures built on the notion of text compression have been used for the comparison and classification of entire genomes and mitochondrial genomes. The present study was undertaken in order to explore their utility in the classification of protein sequences. RESULTS: We constructed compression-based distance measures (CBMs) using the Lempel-Zlv and the PPMZ compression algorithms and compared their performance with that of the Smith-Waterman algorithm and BLAST, using nearest neighbour or support vector machine classification schemes. The datasets included a subset of the SCOP protein structure database to test distant protein similarities, a 3-phosphoglycerate-kinase sequences selected from archaean, bacterial and eukaryotic species as well as low and high-complexity sequence segments of the human proteome, CBMs values show a dependence on the length and the complexity of the sequences compared. In classification tasks CBMs performed especially well on distantly related proteins where the performance of a combined measure, constructed from a CBM and a BLAST score, approached or even slightly exceeded that of the Smith-Waterman algorithm and two hidden Markov model-based algorithms.

Algorithms↗

PubMed2005

Accuracy of frozen section in distinguishing primary ovarian neoplasia from tumors metastatic to the ovary.

Frozen section is widely used in the intra-operative assessment of patients with ovarian tumors. The diagnosis of malignancy is usually straightforward but in some cases it may be difficult to distinguish whether tumors are of ovarian origin or represent matastases from other sites. Recently, Seidman and colleagues presented a simple algorithm based on tumor size and unilateral versus bilateral involvement to aid in intra-operative assessment of ovarian mucinous neoplasms. In this study we have reviewed the accuracy of frozen section in distinguishing primary ovarian malignancies from tumors metastatic to the ovaries encountered in two hospitals over a 5-year period. The algorithm was also applied to our cases retrospectively irrespective of histological type. Nine hundred fourteen ovarian frozen sections were performed in the study period including 266 cases with a final diagnosis of malignancy. Thirty-seven malignancies (13.9%) were of metastatic origin (exclusing one lymphoma), 21 of which (58.8%) were correctly identified on frozen section. In 5 additional cases metastatic origin was included in the differential diagnosis while a primary ovarian tumor was favored un 11 cases (29.7%). Application of the algorithm to the metastatic tumors led to correct classification in 26/33 (78.8%) assessable cases. Conversely, 195/228 primary ovarian malignancies were correctly identified intra-operatively but the possibility of extra-ovarian malignancy was considered or not excluded in 33 cases (14.5%). Application of the algorithm to the latter problematic primary ovarian tumors overall was not helpful in distinguishing primary or metastatic origin. However if only low-grade primary adenocarcinomas were considered then 10/12 assessable cases were correctly assigned. In conclusion frozen section is only moderately successful in distinguishing primary ovarian malignancies fron tumors metastatic to the ovaries. The simple algorithm proposed by Seidman and colleagues for assessment of ovarian mucinous tumors is helpful and can be applied to low-grade adenocarcinomas of other histological types.

Algorithms↗