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Results for “Multilayer Perceptrons”

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PubMed1999

Application of two neural network paradigms to the study of voluntary employee turnover.

Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

Adult↗

PubMed2000

Validation of a decision support system for the cytodiagnosis of fine needle aspirates of the breast using a prospectively collected dataset from multiple observers in a working clinical environment.

We have used a 692 case dataset, collected retrospectively by a single observer, to develop decision support systems for the cytodiagnosis of fine needle aspirates of breast lesions. In this study, we use a 322 case dataset that was prospectively collected by multiple observers in a working clinical environment to test two predictive systems, using logistic regression and the multilayer perceptron (MLP) type of neural network. Ten observed features and the patient age were used as input features. The systems were developed using a training set and test set from the single observer dataset and then applied to the multiple observer dataset. For the independent test cases from the single observer dataset, with a threshold set for no false positives on the training set, logistic regression produced a sensitivity of 82% (95% confidence interval 73-91) and a predictive value of a positive result (PV +) of 98% (95-99), the values for the MLP were 79% (69-89) and 100%, respectively. However the performance on the prospective multiple observer dataset was much worse, with a sensitivity of 72% (65-80), and PV + of 97% (94-99) for logistic regression and 67% (60-75) and 91% (85-97) for the MLP. These results suggest that there is considerable interobserver variability for the defined features and that this system is unsuitable for further development in the clinical environment unless this problem can be overcome.

Adolescent↗

PubMed1998

Prediction of anticoagulation during hemodialysis by population kinetics and an artificial neural network.

All systems currently used for routine hemodialysis require heparin administration to prevent blood clotting in the extracorporeal circuit. We tested the hypothesis that population-based statistical techniques can be used to predict heparin concentrations during routine hemodialysis. Two predictive models were developed, one based on nonlinear mixed effects modeling (NONMEM) and the other on a multilayer perceptron neural network. Serial clotting time data were obtained from forty-nine patients and used to develop the models. The models were used to predict the clotting times of 70 patients in a prospective test. We determined that the neural network provided greater precision, had fewer outliers in its predictions, and did not have the model misspecification in bolus administration that the NONMEM predictions demonstrated. A final NONMEM model was developed using all data from 119 patients to identify important covariates for predicting the heparin pharmacodynamic parameters, volume of distribution, and clearance. Both the volume of distribution and clearance increased following the initiation of dialysis and as the patient's baseline clotting time increased. The volume of distribution also increased as the patient's weight increased but was decreased by smoking and diabetes. Population-based statistical techniques may provide a useful alternative to existing methods for prescribing heparin.

Adult↗

PubMed2001

Artificial neural network-derived trends in daily maximum surface ozone concentrations.

Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.

Air Pollution↗

PubMed2004

Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design.

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

Algorithms↗

PubMed2002

Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms.

Two different machine-learning algorithms have been used to predict the blood-brain barrier permeability of different classes of molecules, to develop a method to predict the ability of drug compounds to penetrate the CNS. The first algorithm is based on a multilayer perceptron neural network and the second algorithm uses a support vector machine. Both algorithms are trained on an identical data set consisting of 179 CNS active molecules and 145 CNS inactive molecules. The training parameters include molecular weight, lipophilicity, hydrogen bonding, and other variables that govern the ability of a molecule to diffuse through a membrane. The results show that the support vector machine outperforms the neural network. Based on over 30 different validation sets, the SVM can predict up to 96% of the molecules correctly, averaging 81.5% over 30 test sets, which comprised of equal numbers of CNS positive and negative molecules. This is quite favorable when compared with the neural network's average performance of 75.7% with the same 30 test sets. The results of the SVM algorithm are very encouraging and suggest that a classification tool like this one will prove to be a valuable prediction approach.

Algorithms↗

PubMed1996

A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytometry data.

Four artificial neural network paradigms (multilayer perceptron networks, learning vector quantization networks, and radial and asymmetric basis function networks) and two statistical methods (parametric statistical classification by modelling each class with Gaussian distributions, and non-parametric density estimation via the K-nearest neighbour method) were compared for their ability to identify seven freshwater and five marine phytoplankton species from flow cytometric data. Kohonen self-organizing maps were also used to examine similarities between species. Optimized networks and statistical methods performed similarly, correctly identifying between 86.8% and 90.1% of data from freshwater species, and between 81.3% and 84.1% of data from marine species. Choice of identification technique must therefore be made on the basis of other criteria. We highlight the way each method partitions the data space and thereby separates the data clusters, and discuss the relative merits of each with reference to complexity of data boundaries, training time, analysis time and behaviour when presented with 'novel' data.

Biometry↗

PubMed2025

HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.

MOTIVATION: Accurate tumor subtype diagnosis is crucial for precision oncology, yet current methodologies face significant challenges. These include balancing model accuracy with interpretability and the high costs of generating multi-omics data in clinical settings. Moreover, there is a lack of validated models capable of classifying hierarchical tumor subtypes across a comprehensive pan-cancer cohort. RESULTS: We present a graph neural network, HallmarkGraph, the first biologically informed model developed to classify hierarchical tumor subtypes in human cancer. Inspired by cancer hallmarks, the model's architecture integrates transcriptome profiles and gene regulatory interactions to perform multi-label classification. We evaluate the model on a comprehensive pan-cancer cohort comprising 11 476 samples from 26 primary cancers with 405 subtypes up to eight levels. The model demonstrates exceptional performance, achieving 5-fold cross-validation accuracy between 85% and 99% for tumor subtypes labeled with increasing details of genomic information. It also shows good generalizability on a validation dataset of 887 samples, assessed using three metrics that consider tumor subtypes at individual, combined, and sample levels. Benchmarking and ablation experiments show that hallmark-based embeddings slightly influence model performance, while the integrated multilayer perceptron plays a significant role in determining classifier accuracy. Additionally, we use the SHAP method to link cancer hallmarks with genes, identifying key features that influence model decisions. Our findings present a biologically informed machine learning framework capable of tracking tumor transcriptomic trajectories and distinguishing inter- and intra-tumor heterogeneity in pan-cancer. This approach holds promise for enhancing cancer diagnostics. AVAILABILITY AND IMPLEMENTATION: HallmarkGraph is accessible at https://github.com/laixn/HallmarkGraph.

Humans↗

PubMed2000

Visual confrontation naming and hippocampal function: A neural network study using quantitative (1)H magnetic resonance spectroscopy.

Prior research on the relationship between visual confrontation naming and hippocampal function has been inconclusive. The present study examined this relationship using quantitative (1)H magnetic resonance spectroscopy ((1)H-MRS) to operationalize the function of the left and right hippocampi. The 60-item Boston Naming Test (BNT) was used to measure naming. Our sample included 46 patients with medically intractable, focal mesial temporal lobe epilepsy who had been screened for all pathology other than mesial temporal sclerosis. Statistics included Pearson correlations and neural network analysis (multilayer perceptron and radial basis function). Baseline BNT performance correlated significantly with left (1)H-MRS hippocampal ratios. Thirty-six per cent of the variance in baseline BNT performance was explained by a neural network model using left and right (1)H-MRS ratios(creatine/N-acetylaspartate) as input. This was elevated to 49% when input from the right hippocampus was lesioned mathematically. In a second model, left (1)H-MRS hippocampal ratios were modelled using measures of semantic and episodic memory as input (including the BNT). Explained variance in left (1)H-MRS hippocampal ratios fell from 60.8 to 3.6% when input from BNT and another semantic memory measure was degraded mathematically. These results provide evidence that the speech-dominant hippocampus is a significant component of the overall neuroanatomical network of visual confrontation naming. Clinical and theoretical implications are explored.

Adult↗

PubMed2002

Bullet trajectory predicts the need for damage control: an artificial neural network model.

BACKGROUND: Effective use of damage control in trauma hinges on an early decision to use it. Bullet trajectory has never been studied as a marker for damage control. We hypothesize that this decision can be predicted by an artificial neural network (ANN) model based on the bullet trajectory and the patient's blood pressure. METHODS: A multilayer perceptron ANN predictive model was developed from a data set of 312 patients with single abdominal gunshot injuries. Input variables were the bullet path, trajectory patterns, and admission systolic pressure. The output variable was either a damage control laparotomy or intraoperative death. The best performing ANN was implemented on prospectively collected data from 34 patients. RESULTS: The model achieved a correct classification rate of 0.96 and area under the receiver operating characteristic curve of 0.94. External validation showed the model to have a sensitivity of 88% and specificity of 96%. Model implementation on the prospectively collected data had a correct classification rate of 0.91. Sensitivity analysis showed that systolic pressure, bullet path across the midline, and trajectory involving the right upper quadrant were the three most important input variables. CONCLUSION: Bullet trajectory is an important, hitherto unrecognized, factor that should be incorporated into the decision to use damage control.

Abdominal Injuries↗

PubMed2003

An artificial neural network for prostate cancer staging when serum prostate specific antigen is 10 ng./ml. or less.

PURPOSE: An artificial neural network was developed to improve the prediction of pathological stage before radical prostatectomy based on variables available at biopsy and clinical parameters. MATERIALS AND METHODS: We used the prospectively accrued European prostate cancer detection data base to train an artificial neural network to predict pathological stage in 200 men with serum prostate specific antigen (PSA) 10 ng./ml. or less who underwent radical prostatectomy. Variables included in the artificial neural network were patient age, serum PSA, free-to-total PSA ratio, PSA velocity, transrectal ultrasound calculated total and transition zone volumes with their associated PSA parameters (transition zone PSA density and PSA density), digital rectal examination and Gleason score on biopsy. Two multilayer perceptron neural networks were trained on the remaining variables. Data on the 200 patients were divided randomly into a training set, a test set and a validation or prospective set. RESULTS: Overall classification accuracy of the artificial neural network was 92.7% and 84.2% for organ confined and advanced prostate cancer staging, respectively. For preoperatively predicting local versus advanced stage the area under the ROC curve for the artificial neural network was significantly larger (0.91) compared with logistic regression analysis (0.83), Gleason score (0.69), PSA density (0.68), prostate transition zone volume (0.63) and serum PSA (0.62) (all p <0.01). CONCLUSIONS: The artificial neural network outperformed logistic regression analysis and correctly predicted pathological stage in more than 90% of the validation patients with serum PSA 10 ng./ml. or less based on clinical, biochemical and biopsy data. In the future artificial neural networks may represent a significant step for improved staging of prostate cancer when counseling patients referred for radical prostatectomy or other curative treatments.

Adult↗

PubMed2004

Constraining the topology of neural networks to ensure dynamics with symmetry properties.

This paper addresses the training of network models from data produced by systems with symmetry properties. It is argued that although general networks are global approximators, in practice some properties such as symmetry are very hard to learn from data. In order to guarantee that the final network will be symmetrical, constraints are developed for two types of models, namely, the multilayer perceptron (MLP) network and the radial basis function (RBF) network. In global modeling problems it becomes crucial to impose conditions for symmetry in order to stand a chance of reproducing symmetry-related phenomena. Sufficient conditions are given for MLP and RBF networks to have a set of fixed points that are symmetrical with respect to the origin of the phase space. In the case of MLP networks such conditions reduce to the absence of bias parameters and the requirement of odd activation functions. This turns out to be important from a dynamical point of view since some phenomena are only observed in the context of symmetry, which is not a structurally stable property. The results are illustrated using bench systems that display symmetry, such as the Duffing-Ueda oscillator and the Lorenz system.

Algorithms↗

PubMed1994

NNERVE: neural network extraction of repetitive vectors for electromyography--Part I: Algorithm.

Artificial neural network (ANN) based signal processing methods have been shown to have significant robustness in processing complex, degraded, noisy, and unstable signals. A novel approach to automated electromyogram (EMG) signal decomposition, using an ANN processing architecture, is presented in this paper. Due to the lack of a priori knowledge of motor unit action potential (MUAP) morphology, the EMG decomposition must be performed in an unsupervised manner. An ANN classifier, consisting of a multilayer perceptron neural network and employing a novel unsupervised training strategy, is proposed. The ANN learns repetitive appearances of MUAP waveforms from their suspected occurrences in a filtered EMG signal in an autoassociative learning task. The same training waveforms are fed into the trained ANN and the output of the ANN is fed back to its input, giving rise to a dynamic retrieval net classifier. For each waveform in the data, the network discovers a feature vector associated with that waveform. For each waveform, classification is achieved by comparing its feature vector with those of the other waveforms. Firing information of each MUAP is further used to refine the classification results of the ANN classifier. Then, individual MUAP waveform shapes are derived and their firing tables are created.

Algorithms↗

PubMed1998

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer.

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Algorithms↗

PubMed1999

Three machine learning techniques for automatic determination of rules to control locomotion.

Automatic prediction of gait events (e.g., heel contact, flat foot, initiation of the swing, etc.) and corresponding profiles of the activations of muscles is important for real-time control of locomotion. This paper presents three supervised machine learning (ML) techniques for prediction of the activation patterns of muscles and sensory data, based on the history of sensory data, for walking assisted by a functional electrical stimulation (FES). Those ML's are: 1) a multilayer perceptron with Levenberg-Marquardt modification of backpropagation learning algorithm; 2) an adaptive-network-based fuzzy inference system (ANFIS); and 3) a combination of an entropy minimization type of inductive learning (IL) technique and a radial basis function (RBF) type of artificial neural network with orthogonal least squares learning algorithm. Here we show the prediction of the activation of the knee flexor muscles and the knee joint angle for seven consecutive strides based on the history of the knee joint angle and the ground reaction forces. The data used for training and testing of ML's was obtained from a simulation of walking assisted with an FES system [39]. The ability of generating rules for an FES controller was selected as the most important criterion when comparing the ML's. Other criteria such as generalization of results, computational complexity, and learning rate were also considered. The minimal number of rules and the most explicit and comprehensible rules were obtained by ANFIS. The best generalization was obtained by the IL and RBF network.

Algorithms↗

PubMed2000

Staging of cervical cancer with soft computing.

This paper describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer. Hybridization includes the evolution of knowledge-based subnetwork modules with genetic algorithms (GA's) using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm. Crude subnetworks obtained via rough set theory and the ID3 algorithm are evolved using GA's. The evolution uses a restricted mutation operator which utilizes the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional multilayer perceptron. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for extraction of logical rules and human interpretation of the inferencing procedure.

Algorithms↗

PubMed2001

Artificial neural networks for discriminating pathologic from normal peripheral vascular tissue.

The identification of the state of human peripheral vascular tissue by using artificial neural networks is discussed in this paper. Two different laser emission lines (He-Cd, Ar+) are used to excite the chromophores of tissue samples. The fluorescence spectrum obtained, is passed through a nonlinear filter based on a high-order (HO) neural network neural network (NN) [HONN] whose weights are updated by stable learning laws, to perform feature extraction. The values of the feature vector reveal information regarding the tissue state. Then a classical multilayer perceptron is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered. Our method achieves not only the discrimination between normal and pathologic human tissue, but also the successful discrimination between the different types of pathologic tissue (fibrous, calcified). Furthermore, the small time needed to acquire and analyze the fluorescence spectra together with the high rates of success, proves our method very attractive for real-time applications.

Arteriosclerosis↗

PubMed2001

ECG beat recognition using fuzzy hybrid neural network.

This paper presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.

Algorithms↗