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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↗

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↗

PubMed2001

Molecular classification of human carcinomas by use of gene expression signatures.

Classification of human tumors according to their primary anatomical site of origin is fundamental for the optimal treatment of patients with cancer. Here we describe the use of large-scale RNA profiling and supervised machine learning algorithms to construct a first-generation molecular classification scheme for carcinomas of the prostate, breast, lung, ovary, colorectum, kidney, liver, pancreas, bladder/ureter, and gastroesophagus, which collectively account for approximately 70% of all cancer-related deaths in the United States. The classification scheme was based on identifying gene subsets whose expression typifies each cancer class, and we quantified the extent to which these genes are characteristic of a specific tumor type by accurately and confidently predicting the anatomical site of tumor origin for 90% of 175 carcinomas, including 9 of 12 metastatic lesions. The predictor gene subsets include those whose expression is typical of specific types of normal epithelial differentiation, as well as other genes whose expression is elevated in cancer. This study demonstrates the feasibility of predicting the tissue origin of a carcinoma in the context of multiple cancer classes.

Carcinoma↗

PubMed1998

Application of fuzzy-classifier system to coronary artery disease and breast cancer.

This paper presents an application of a genetic-algorithm-based representation of fuzzy rules for the classification of coronary artery disease data and breast cancer data. The performance of this fuzzy classifier for classification of coronary artery disease and breast cancer data is evaluated. In this study the concept of fuzzy if-then has been applied of rules proposed by Ishibuchi et al. for a multi dimensional data classification problem which leads to higher classification power. The fitness value of each fuzzy if-then rule was determined by the numbers of correctly and wrongly classified training patterns for that rule. The classification power on real world data for coronary artery disease and breast cancer was thus demonstrated by computer simulations.

Algorithms↗

PubMed1995

Lesion size quantification in SPECT using an artificial neural network classification approach.

An artificial neural network (ANN) has been developed to determine the size of lesions detected in single photon emission computed tomographic images. The network is the Learning Vector Quantizer and is trained to perform size quantification based on image neighborhoods extracted around the lesions. The ANN is compared to the optimal, Bayesian algorithm developed to perform the same task using the unreconstructed, projection data. The performance of the neural network is evaluated at two different noise levels. The Bayesian algorithm provides the upper bound for size quantification performance against which the ANN is compared. In the ideal case where the Bayesian algorithm has explicit knowledge of the underlying distributions, its performance is superior to that of the neural network. However, in the more realistic case where the distributions need to be estimated from the same learning sample the ANN was trained on, the two algorithms have comparable performances.

Algorithms↗

PubMed1997

Neural network method to determine the vigilance levels of the central nervous system, related to occupational chronic chemical stress.

The effects of chronic toxic occupational factors and functional disorders of the central nervous system (CNS) in chemical industry were studied. These factors cause various stages of chronic chemical stress on the human CNS together with changes of the vigilance levels. On the basis of QEEG data analysis and psychometric tests we identified three stages of occupational chemical stress syndromes according to the CNS vigilance level (ordered from light to severe): hypersthenic syndrome, hyposthenic syndrome, and organic psychosyndrome. Each syndrome is characterized by specific changes in the QEEG data. A perceptron-based neural network was developed for the classification of the QEEG data to one of the above-mentioned syndrome classes. The data of 77 patients and 10 healthy subjects were selected to test the algorithm. Different combinations of the QEEG data as input features to the classifier were chosen. The most reliable classification was obtained when QEEG data measured during the visual stimulation of the CNS were used. However, sometimes the algorithm was unable to solve the classification problem, or it took a very long time to train the perceptron. In part, difficulties arose from using a perceptron-based algorithm, which can classify only linearly separable data.

Algorithms↗

PubMed1984

Differential diagnosis of jaundice: a pocket diagnostic chart.

Based on extensive clinical and clinical chemical information (107 different items) from 1002 jaundiced patients, we developed a diagnostic algorithm which was evaluated on a test sample of another 110 jaundiced patients. A primary classification into categories of obstructive jaundice (probability of obstruction greater than or equal to 0.80), non-obstructive jaundice (probability of obstruction less than or equal to 0.20), and of doubtful causes of jaundice (probability of obstruction: 0.20-0.80) was attempted. Among 234 patients in the data base who were classified as obstructive, 220 (94%) proved to be so, as did 36 (97%) of 37 in the test sample. The corresponding figures for non-obstructive jaundice were 463 (96%) of 483 patients correctly classified in the data base and 47 (92%) of 51 patients in the test sample. Altogether 69% of the patients in the data base and 75% of those in the test sample were correctly classified, in 27% and 20% the cause of jaundice was doubtful, and only 4% and 5%, respectively, were misclassified. A slight majority of the patients in whom the algorithmic diagnoses were doubtful proved obstructive. A close correlation was found between the preliminary diagnoses made by the algorithm and by the clinicians. A secondary classification of the patients by the algorithm into benign versus malignant causes of obstructive jaundice performed equally well in the data base and the test sample.

Cholestasis↗

PubMed2001

Potential of the genetic algorithm neural network in the assessment of gait patterns in ankle arthrodesis.

The aim of this study was to develop an empirical model of parameter-based gait data, based on an artificial neural network and a genetic algorithm, for the assessment of patients after ankle arthrodesis. Ground reaction force vectors were measured by force platforms during level walking. Nine force parameters expressed in percentage of body weight and their chronologic incidence of occurrence expressed in percentage of stance phase period were used in modeling. Ten healthy persons and ten patients who had solid arthrodesis of the ankle were recruited in this study for developing the model. By applying the genetic algorithm neural network, the percentage of correct classification was 98.8% and the subset of discriminant parameters was be reduced to 9 out of 18. These key parameters were mainly related to the loading response and propulsive phase. This indicates that there was a reduction in the abilities in cushion impact and push off in the patients after ankle arthrodesis. Finally, the relative distance (Dr) was defined in this study and used in two new patients' examinations to demonstrate its clinical utility.

Adult↗

PubMed1998

Image analysis and pattern recognition for computer supported skin tumor diagnosis.

A new approach to computer supported recognition of melanoma and naevocytic naevi based on high resolution skin surface profiles is presented. Profiles are generated by sampling an area of 4 x 4 mm2 at a resolution of 125 sample points per mm with a laser profilometer at a vertical resolution of 0.1 micron. With image analysis algorithms Haralick's texture parameters, Fourier features and features based on fractal analysis are extracted. Genetic algorithms are employed successfully to select good feature subsets for the following classification process. As quality measure for feature subsets, the error rate of the nearest neighbor classifier estimated with the leaving-one-out method is used. Classification is performed with feed forward back-propagation network and the nearest neighbor classifier. Classification performance of the neural classifier is optimized using different topologies, learning parameters and pruning algorithms. The best neural classifier achieved an error rate of 4.5% and was found after network pruning. The best result with an error rate of 2.3% was obtained with the nearest neighbor classifier.

Algorithms↗

PubMed1995

A PC based neural network algorithm for measurement of heart rate variability.

Heart Rate Variability has recently been shown as a viable index to predict sudden cardiac death. The goal of this research is to investigate the use of neural network technique to classify detected QRS complexes into normal and abnormal ones. A single layer perceptron neural network is used for this QRS pattern learning and classification. Results with real data showed that the algorithm gives a 99% correct QRS detection rate.

Algorithms↗

PubMed1993

Algorithms for the diagnosis and management of idiopathic anaphylaxis.

A major approach to the prevention of anaphylaxis due to exposure to an allergen is avoidance of the allergens. In cases of idiopathic anaphylaxis (IA), the method of management must first be pharmacologic control of the acute episode of IA and then the prevention of recurrent episodes. The appropriate management requires diagnosis, classification, and immediate initiation of a treatment regimen that will control and then induce a remission in patients with IA of the frequent (F) type. The classification and management of IA are reviewed and algorithms are presented for initial and long-term management of patients with IA.

Algorithms↗

PubMed1997

New objective classification system for nuclear opacification.

We have developed an autonomous objective classification scheme for degree of nuclear opacification. The algorithm was developed by using a series of color 35-mm slides acquired with a Topcon photo slit-lamp microscope and use of standard camera settings. The photographs were digitized, and first, and second-order gray-level statistics were extracted from within circular regions of the nucleus. Classifications of severity were performed by using these features as input to a neural network. Training versus classification performance was tested by using photographs of different eyes, and test/retest classification reproducibility was evaluated by using paired photographs of the same eyes. We demonstrate good performance of the classifier against subjective assessments rendered by the Wilmer grading system [Invest. Ophthalmol. Visual Sci. 29, 73 (1988)] and markedly better test/retest reproducibility.

Algorithms↗

PubMedDate not supplied

[Numerical taxonomy of the genus Desulfovibrio by group analysis].

The Desulfovibrio genus has a particular interest because it includes the microorganisms connected with the corrosion produced microbiologically. The taxonomy of the genus shows disadvantages due to its metabolical and physiological characteristics. In this paper, 14 strains of the Desulfovibrio type were studied from the metabolical point of view. Numeric taxonomy was carried out according to the Group Analysis method, using and comparing the change possibilities of the method. The Consensus Method was also applied. The results obtained indicate a low metabolic activity of the strains with regard to the number of compounds which can be used as energy source. The taxonomic method showed a better structure with more clear divisions, corresponding to Simple Matching coefficient (which coincides with other symmetric coefficients and with the distance coefficient) with average bond (UPGMA). It is estimated that the present classification will vary in time with new strains with different metabolic characteristics. The two groups of bacteria correspond to those with more and less degrading ability.

Algorithms↗

PubMed2000

Minimizing stochastic complexity using local search and GLA with applications to classification of bacteria.

In this paper, we compare the performance of two iterative clustering methods when applied to an extensive data set describing strains of the bacterial family Enterobacteriaceae. In both methods, the classification (i.e. the number of classes and the partitioning) is determined by minimizing stochastic complexity. The first method performs the minimization by repeated application of the generalized Lloyd algorithm (GLA). The second method uses an optimization technique known as local search (LS). The method modifies the current solution by making global changes to the class structure and it, then, performs local fine-tuning to find a local optimum. It is observed that if we fix the number of classes, the LS finds a classification with a lower stochastic complexity value than GLA. In addition, the variance of the solutions is much smaller for the LS due to its more systematic method of searching. Overall, the two algorithms produce similar classifications but they merge certain natural classes with microbiological relevance in different ways.

Algorithms↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗