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PubMed2005

Differences in biological features of gastric dysplasia, indefinite dysplasia, reactive hyperplasia and discriminant analysis of these lesions.

AIM: To investigate the differences in biological features of gastric dysplasia (Dys), indefinite dysplasia (IDys) and reactive hyperplasia (RH) by studying the biomarker alterations in cell proliferation, cell differentiation, cell cycle control and the expression of house-keeping genes, and further to search for markers which could be used in guiding the pathological diagnosis of three lesions. METHODS: Expressions of MUC5AC, MUC6, adenomatous polyposis coli (APC), p53, Ki-67, proliferation cell nuclear antigen (PCNA) and EGFR were studied by immunohistochemistry with a standard Envision technique in formalin-fixed and paraffin-embedded specimens from 43 RH, 35 IDys, 35 Dys and 36 intestinal type gastric carcinomas (IGC). In addition, Bayes discriminant analysis was used to investigate the value of markers studied in differential diagnosis of RH, IDys, Dys and IGC. RESULTS: The MUC5AC and MUC6 antigen expressions in RH, IDys, Dys and IGC decreased gradually (MUC5AC: 86.04%, 77.14%, 28.57%, 6.67%; MUC6: 65.15%, 54.29%, 20.00%, 25.00%, respectively). The expressions of the two markers had no significant difference between RH and IDys, but were all significantly higher than those of the other two lesions (MUC5AC: chi2=27.607, 38.027 and 17.33, 26.092; MUC6: chi2=16.54, 12.665 and 9.282, 6.737, P<0.01). There was no significant difference between RH and IDys, Dys and IGC in MUC6 expression. The APC gene expression in the four lesions had a similar decreasing tendency (RH 69.76%, IDys 68.57%, Dys 39.39%, IGC 22.86%), and it was significantly higher in the first two lesions than in the last two (chi2=7.011, 16.995 and 14.737, 19.817, P<0.05). The p53 expression in RH, IDys, Dys and IGC was 6.98%, 20%, 57.14% and 50%, respectively. There was no significant difference between RH and IDys or Dys and IGC, but the p53 expression in RH and IDys was significantly lower than that in Dys and IGC (chi2=7.011, 16.995 and 14.737, 19.817, P<0.01). The Ki-67 label index was significantly different among four lesions (RH: 0.298+/-8.92%, IDys: 0.358+/-9.25%, Dys: 0.498+/-9.03%, IGC: 0.620+/-10.8%, P<0.001). Positive immunostaining of PCNA was though observed in all specimens, significant differences were detected among four lesions (F=95.318, P<0.01). In addition, we used Bayes discriminant analysis to investigate molecular pathological classification of the lesions, and obtained the best result with the combination of MUC5AC, Ki-67 and PCNA. The overall rate of correct classification was 67.4% (RH), 68.6% (IDys), 70.6% (Dys) and 84.8% (IGC), respectively. CONCLUSION: Dys has neoplastic biological characteristics, while RH and IDys display hyperplastic characteristics. MUC5AC and proliferation-related biomarkers (Ki-67, PCNA) are more specific in distinguishing Dys from RH and IDys.

Biomarkers↗
PubMed2001

Screening of tuberculous pleural effusion by discriminant analysis.

SETTING: Pneumology Department of a 635-bed acute-care teaching hospital in Valencia, Spain. OBJECTIVE: To assess the value of discriminant analysis as a method of optimizing the discriminant power of routine radiographic features and a panel of laboratory parameters including biochemical analyses of pleural fluid for differentiation between tuberculous and non-tuberculous pleural effusion. DESIGN: A series of 47 variables were retrospectively obtained from the medical records of 189 patients with exudative pleural effusion (tuberculous pleurisy, n = 78; non-tuberculous pleurisy, n = 111). A backward elimination method was applied until the best discriminant function was found. RESULTS: The most powerful predictor of tuberculous pleural effusion was a function that consisted of four variables, as follows: age (years); tuberculin skin test (mm of induration at 48 hours); white blood cell count (cells/mm3); and bloodstained exudate (coded as yes = 1, no = 2). This function showed a sensitivity of 90%, specificity of 87%, positive predictive value of 83%, negative predictive value of 92%, and was able to give an 88% rate of correct classification. CONCLUSION: The calculated discriminant function based on the patient's age, peripheral leukocyte count, tuberculin skin test and blood in the exudate is a simple, rapid and inexpensive method for screening tuberculous etiology in patients with pleural effusion.

Adolescent↗
PubMed1977

Hypercalcaemia in elderly hospital in-patients: value of discriminant analysis in differential diagnosis.

Plasma calcium was measured routinely as a part of profile screening of patients admitted to a geriatric department. Pathological hypercalcaemia was found in 1.33% of those screened, the cause being bone metastases (29%), hyperparathyroidism (21%), bronchial carcinoma without bone metastasis (18.5%), lymphosarcoma without bone metastasis (8%) and multiple myeloma (2.5%). There remained a further group of patients with hypercalcaemia and renal failure (21%) in whom diagnosis was often obscure. Where renal function was normal, discriminant analysis showed that the four main diagnostic groups were biochemically distinguishable. Discriminant analysis thus seems likely to be of practical value in the differential diagnosis of hypercalcaemia in elderly patients with normal renal function, but requires prospective validation.

Aged↗
PubMedDate not supplied

Meat species identification by linear discriminant analysis of capillary electrophoresis protein profiles.

The objective of this study was to utilize linear discriminant analysis (LDA) in the interpretation of capillary electrophoresis-sodium dodecyl sulfate polymer-filled capillary gel electrophoresis (CE-SDS) meat protein profiles for the identification of meat species. The specific objectives were 1) to collect quantitative data on water-soluble and saline-soluble proteins of different meat species obtained by CE-SDS and 2) to apply LDA on collected CE-SDS protein data for the development of a pattern recognition statistical model useful in the differentiation of meat species. Samples were raw beef top and eye round, boneless fresh pork ham and loin, turkey leg and breast meat, and mechanically deboned turkey meat collected on six different occasions, making a total of 42 samples. Additionally, 14 samples were used as test samples to determine the classification ability of the procedure. Quantitative protein data obtained by CE-SDS was used to generate separate LDA models for either water- or saline-soluble protein extracts. Although a saline solution was a more efficient meat protein-extracting agent, as shown by a higher total protein concentration and a larger number of peaks, water-soluble CE-SDS protein profiles gave more distinctive discrimination among meat species. The correct classification given by LDA on water-soluble protein data was 100% for all meat species, except pork (94%). Conversely, the correct classification on saline-soluble protein data was 88% for beef and mechanically deboned turkey meat, and 94% and 100% for turkey and pork meat, respectively. LDA proved to be a useful pattern recognition procedure in the interpretation of CE-SDS protein profiles for the identification of meat species.

Animals↗
PubMed1981

[Discriminant analysis of age parameters of the rat (author's transl)].

The capacity of age parameters to discriminate between groups or individuals of different biological age was investigated in the course of a long-term cohort study. 23 age parameters measured in 71 male Sprague-Dawley rats at the ages of 10, 17, 25 and 30 months, were submitted to a stepwise discriminant analysis. An optimal simultaneous discrimination between the four age groups was obtained by 3 discriminant functions with scores of 89.8%, 6.2% and 4%, with a correct classification of 98,6% of all individuals. Three analyses of successive age groups (10--17, 17--25, 25--30 months) resulted in a 100% correct classification by one function each. However, it turned out that in different phases of senescence an optimal discrimination between individuals or groups of different biological age is obtained by distinct combinations and weights of the parameters.

Aging↗
PubMed1997

Classification and staging of dementia of the Alzheimer type: a comparison between neural networks and linear discriminant analysis.

OBJECTIVE: To examine the utility of artificial neural networks (ANNs) for differentiating patients with Alzheimer disease from healthy control subjects and for staging the degree of dementia. DESIGN: Comparison of the classification abilities of ANNs with the statistical technique of linear discriminant analysis (LDA) using the results of 11 neuropsychological tests as predictors. PARTICIPANTS: Ninety-two patients with a diagnosis of probable Alzheimer disease (referred from a geriatric clinic) and 43 elderly control subjects (independently solicited). The patients met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria for probable dementia, with clinical ratings of dementia severity derived from the Cambridge Examination for Mental Disorders of the Elderly (CAMDEX). MAIN OUTCOME MEASURES: Classifications between and within groups were determined by using LDA and ANNs, and more detailed comparisons of the 2 methods were performed by using chi2 analyses and unweighted and weighted kappa statistics. RESULTS: Linear discriminant analysis correctly identified 71.9% of cases. Artificial neural networks, trained to classify the subjects using the same data, correctly classified 91.1% of the cases. Subsidiary analyses showed that although both techniques effectively discriminated between the control subjects and patients with dementia, the ANNs were more powerful in discriminating severity levels within the dementia population. The analyses for goodness of fit revealed that the ANN classification produced a better fit to the actual data. A comparison of the weighted proportion of agreement between the criterion and predictor variables also showed that the ANNs clearly outperformed LDA in classification accuracy for the full data set and patients-only data set. CONCLUSION: The results demonstrate the utility of ANNs for group classification of patients with Alzheimer disease and elderly controls and for staging dementia severity using neuropsychological data.

Aged↗
PubMed1983

Accurate prediction of duodenal-ulcer healing rate by discriminant analysis.

Previous studies have shown that approximately 50% and 70% of duodenal ulcers heal after 2 and 4 wk, respectively, of cimetidine, and at least one-third heal after 4 wk of placebo. In order to identify these groups of ulcers before treatment, a two-phase study was performed, including an initial double-blind trial of cimetidine vs. placebo in 120 patients, and a subsequent open study with identical protocol of cimetidine vs. no cimetidine in another 60 patients. Forty clinical, personal, physiologic, and endoscopic characteristics were prospectively obtained in each patient, and were analyzed by stepwise discriminant analysis at the end of phase 1. This identified the discriminants against healing after 2 wk of cimetidine as late onset disease, body weight, and ulcer diameter; those after 4 wk of cimetidine as analgesic consumption, neurosis, low fasting serum gastrin, low pentagastrin D50 and ulcer diameter; and those after 4 wk of placebo as back pain, bleeding, and alcohol consumption. Based on the discriminant scores derived, the sensitivity, specificity, and efficiency of prediction for complete healing as determined endoscopically were 74.4%, 90%, and 82.3% for 2-wk cimetidine, 100%, 87.5%, and 97.5% for 4-wk cimetidine, and 85.7%, 83.3%, and 84.2% for 4-wk placebo treatment. In phase 2, correct predictions were made in 36 of 40, 39 of 40, and 17 of 20 patients treated, respectively, for 2 and 4 wk with cimetidine, and 4 wk without cimetidine. Accurate prediction of duodenal-ulcer healing rate with and without cimetidine is thus possible by discriminant analysis. As many medical and surgical modalities of treatment are now available, this approach should have the potential of selecting the appropriate form of treatment for a given patient.

Adolescent↗
PubMed1994

Further studies on the electrodiagnosis of diabetic peripheral polyneuropathy using discriminant function analysis.

Discriminant function analysis can be useful when applied to multiple nerve conduction parameters for diabetic and nondiabetic subjects to reveal the essential dimension along which key neuropathic differences occur between these groups. In this study, 19 electrophysiologic parameters were used in a stepwise discriminant function analysis to reveal a highly significant dimension of intergroup differences between 67 diabetic and 75 normal adult Japanese-American males. The classification functions thereby derived are more sensitive and specific than those reported previously for this population. Furthermore, when 72 additional subjects with impaired glucose tolerance were examined, they showed considerable overlap with the normal and separation from the diabetic groups, respectively. Their intermediate position between normal and diabetics in the key discriminant dimension indicates that essential neuropathic change is, at most, incipient in this latter group.

Cross-Sectional Studies↗
PubMed2005

Incremental linear discriminant analysis for classification of data streams.

This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number of classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.

Algorithms↗
PubMed1985

Discriminant analysis for predicting dystocia in beef cattle. I. Comparison with regression analysis.

Data from 131 calvings of Chianina crossbred cows (2 to 5 yr old) bred to Chianina bulls were used to compare stepwise multiple regression analysis (RA) and stepwise, two-group discriminant analysis (DA) for predicting dystocia. Variables (21) studied in relation to dystocia included both prebreeding and precalving cow and calf effects. Calving was categorized as either unassisted or assisted without regard to the severity of dystocia. During this study, 30 (22.9%) assisted births occurred. All variables were standardized to a mean of zero and a variance of one before statistical analyses. Models were developed based on precalving variables and with both precalving and postcalving variables with both RA and DA. Average discriminant scores (centroids) were different (P less than .01) between assisted and unassisted cows. Significant precalving DA variables were cow age and precalving pelvic height. This model correctly predicted 26 of 30 (86.7%) of the occurrences of dystocia. Significant precalving RA variables were prebreeding pelvic width and precalving pelvic height. The amount of variation accounted for by these two factors was 31.5%. Calf birth weight, calf chest depth, calf height, precalving pelvic area, cow age and precalving cow weight were selected by DA for use in the combined precalving and postcalving prediction model. Calf birth weight was 58% more important than either pelvic size or cow age. Percentage correctly classified with this model was 87.4. Significant postcalving variables selected by RA in order of importance were prebreeding pelvic width, calf birth weight and calf shoulder width (R2 = .399).(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMed2003

Forensic discrimination of photocopy and printer toners II. Discriminant analysis applied to infrared reflection-absorption spectroscopy.

Copy toner samples were analyzed using reflection-absorption infrared microscopy (R-A IR). The grouping of copy toners into distinguishable classes achieved by visual comparison and computer-assisted spectral matching was compared to that achieved by multivariate discriminant analysis. For a data set containing spectra of 430 copy toners, 90% (388/430) of the spectra were initially correctly grouped into the classifications previously established by spectral matching. Three groups of samples that did not classify well contained too few samples to allow reliable classification. Samples from two other pairs of groups were similar and often misclassified. Closer examination of spectra from these groups revealed discriminating features that could be used in separate discriminant analyses to improve classification. For one pair of groups, the classification accuracy improved to 91% (81/89) and 97% (28/29), for the two groups, respectively. The other pair of groups were completely distinguishable from one another. With these additional tests, multivariate discriminant analysis correctly classified 96% of the 430 R-A IR toner spectra into the toner groups found previously by spectral matching.

Journal Article↗
PubMed1997

The relation of neuropsychological measures to levels of cognitive functioning in elderly individuals: a discriminant analysis approach.

This study addresses the question of which tests in a comprehensive neuropsychologicaI test battery best discriminate normal, mildly, and moderate to severely cognitiveIy impaired older participants. Sixty-six geriatric participants were administered a battery of neuropsychological tests as part of an outpatient geriatric clinic evaluation. A discriminant analysis procedure using a consensus rating of global cognitive impairment as the grouping variable was employed to analyze the data. From 15 neuropsychological test measures, five representing the domains of learning and memory, visuospatial and executive functioning were identified as the best predictors of level of cognitive impairment. Findings demonstrate the utility of discriminant function analysis (DFA) procedures for developing reduced-length cognitive batteries that accurately classify participants in terms of levels of cognitive impairment and identify mild dysfunction in participants at risk of cognitive impairment. Further cross-validation studies are needed to confirm the utility of these more circumscribed batteries.

Journal Article↗
PubMedDate not supplied

Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection.

PURPOSE: To improve the reliability, accuracy, and computational efficiency of tissue classification with multispectral sequences [T1, T2, and proton density (PD)], we developed an automated method for identifying training classes to be used in a discriminant function analysis. We compared it with a supervised operator-dependent method, evaluating its reliability and validity. We also developed a fuzzy (continuous) classification to correct for partial voluming. METHOD: Images were obtained on a 1.5 T GE Signa MR scanner using three pulse sequences that were co-registered. Training classes for the discriminant analysis were obtained in two ways. The operator-dependent method involved defining circular ROIs containing 5-15 voxels that represented "pure" samples of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using a total of 150-300 voxels for each tissue type. The automated method involved selecting a large number of samples of brain tissue with sufficiently low variance and randomly placed throughout the brain ("plugs"), partitioning these samples into GM, WM, and CSF, and minimizing the amount of variance within each partition of samples to optimize its "purity." The purity of the plug was estimated by calculating the variance of 8 voxels in all modalities (T1, T2, and PD). We also compared "sharp" (discrete) measurements (which classified tissue only as GM, WM, or CSF) and "fuzzy" (continuous) measurements (which corrected for partial voluming by weighting the classification based on the mixture of tissue types in each voxel). RESULTS: Reliability was compared for the operator-dependent and automated methods as well as for the fuzzy versus sharp classification. The automated sharp classifications consistently had the highest interrater and intrarater reliability. Validity was assessed in three ways: reproducibility of measurements when the same individuals were scanned on multiple occasions, sensitivity of the method to detecting changes associated with aging, and agreement between the automated segmentation values and those produced through expert manual segmentation. The sharp automated classification emerged as slightly superior to the other three methods according to each of these validators. Its reproducibility index (intraclass r) was 0.97, 0.98, and 0.98 for total CSF, total GM, and total WM, respectively. Its correlations with age were 0.54, -0.61, and -0.53, respectively. Its percent agreement with the expert manually segmented tissue for the three tissue types was 93, 90, and 94%, respectively. CONCLUSION: Automated identification of training classes for discriminant analysis was clearly superior to a method that required operator intervention. A sharp (discrete) classification into three tissue types was also slightly superior to one that used "fuzzy" classification to produce continuous measurements to correct for partial voluming. This multispectral automated discriminant analysis method produces a computationally efficient, reliable, and valid method for classifying brain tissue into GM, WM, and CSF. It corrects some of the problems with reliability and computational inefficiency previously observed for operator-dependent approaches to segmentation.

Adult↗
PubMed1986

Relative reliability of three different discriminant analysis methods for detecting PKU gene carriers.

A previously derived discriminant function for detecting classical PKU gene carriers without a priori pedigree probability was reevaluated using a large sample size. The test involves fluorometric measurement of fasting phenylalanine and tyrosine plasma levels. Among 75 controls and 45 known carriers, 95% could be classified as to their carrier status with greater than 98% accuracy. The accuracy of our method in classifying our population compared favorably to that of two other discriminant analysis methods requiring a priori probability.

Biometry↗
PubMed2006

Feature scaling for kernel fisher discriminant analysis using leave-one-out cross validation.

Kernel fisher discriminant analysis (KFD) is a successful approach to classification. It is well known that the key challenge in KFD lies in the selection of free parameters such as kernel parameters and regularization parameters. Here we focus on the feature-scaling kernel where each feature individually associates with a scaling factor. A novel algorithm, named FS-KFD, is developed to tune the scaling factors and regularization parameters for the feature-scaling kernel. The proposed algorithm is based on optimizing the smooth leave-one-out error via a gradient-descent method and has been demonstrated to be computationally feasible. FS-KFD is motivated by the following two fundamental facts: the leave-one-out error of KFD can be expressed in closed form and the step function can be approximated by a sigmoid function. Empirical comparisons on artificial and benchmark data sets suggest that FS-KFD improves KFD in terms of classification accuracy.

Journal Article↗
PubMed1980

Metabolic and cellular profile testing in calves under feedlot conditions, using discriminant analysis to identify calves with low potential for weight gain.

Thirty-eight variables were measured in blood samples of 48 calves at the beginning (day 0) of a feed trial. After 56 days, the calves were assigned (according to weight gain) into three groups: high gainers, medium gainers, and low gainers. Discriminant analysis was used on the variables that were measured to classify the calves into three groups. When the mean values for three overlapping groups of 16 calves each were analyzed, blood urea nitrogen data alone correctly classified 68.7% of the low gainers. Overall, correct classification never exceeded 58.3%. When three nonoverlapping groups of nine calves each were used, inorganic phosphate data (used first and alone) correctly classified 66.7% of the low gainers. After seven steps, 81.5% of the animals were correctly classified, including 88.9% of the low gainers. The two-group discriminant analysis identified 78% of the nine lowest gainers, and 90% of the remaining animals were correctly classified.

Animals↗
PubMed1999

Discriminant analysis of event-related potential curves using smoothed principal components.

Principal component analysis enhanced by the use of smoothing is used in conjunction with discriminant analysis techniques to devise a statistical classification method for the analysis of event-related potential data. A training set of premedication potentials collected from adolescents with attention-deficit hyperactive disorder (ADHD) is used to predict whether adolescents from an independent subject group will respond to long-term medication. Comparison of outcome prediction rates demonstrates that this method, which uses information from the whole ERP curve, is superior to the classification technique currently used by clinicians, which is based on a single ERP curve feature. The need to administer an initial dose of medication to classify patients is also eliminated.

Adolescent↗