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PubMed1992

Electronystagmographic features in some peripheral and central vestibular disorders: application of multiple discriminant analysis of electronystagmographic parameters.

In routine clinical electronystagmographic (ENG) tests (postrotatory, optokinetic, caloric and tracking tests), eye movement signals were analyzed and a multiple discriminant analysis was carried out with the aid of a microcomputer. Six parameters were selected and, based on these, two functions for discriminating between peripheral and central disorders were established. Discrimination between 35 patients with peripheral lesions and 15 patients with central lesions was made with a correct classification rate of 97.1 and 86.7%, respectively. These rates are significantly higher than that of any single ENG test analysis. Our results indicate that the clinical application of ENG can be improved by searching for more sensitive ENG parameters and adopting the comprehensive analysis approach.

Adult↗
PubMed1998

Classification of Spanish Unifloral Honeys by Discriminant Analysis of Electrical Conductivity, Color, Water Content, Sugars, and pH.

To ascertain the most discriminant variables for seven types of Spanish commercial unifloral honeys, stepwise discriminant analysis was performed. Fifteen parameters [pH; water content; electrical conductivity; x, y, and L, chromatic coordinates from the CIE-1931 (xyL) color space; fructose; glucose; sucrose; maltose; isomaltose; maltulose; kojibiose; and the fructose/glucose and glucose/water ratios] were considered. The studied honey types were rosemary, citrus, lavender, sunflower, eucalyptus, heather, and forest. The most discriminant variables, as selected by the multivariate program, were electrical conductivity, color (x, y, L), water content, fructose, and sucrose. All sunflower, eucalyptus, and honeydew honey samples and >90% of the samples from the remaining honey types were correctly classified by using the classification functions devised by the program. The overall proportion of accurately arranged samples was 95.7%. Results were validated by the "jackknifed" procedure and showed that electrical conductivity, color, water content, fructose, and sucrose are highly useful parameters to classify unifloral honeys, although microscopical analysis of honey sediment remains the fundamental tool.

Journal Article↗
PubMed1992

[The multivariate canonical discriminant analysis between physical growth and nutrition status of children and youth].

This paper is on studying the relationship between physical growth and nutrition status of children and youth by using multivariate canonical discriminant analysis. 3,790 boys and girls of 7-19 yrs of age were classified into four groups according to the height-for-weight method. Then, by using seven physical measures as predict variables, canonical discriminant functions were set up for boys and girls respectively. The backward substitutional test showed that the coincidence ate was quite high, 90.90% for boys and 92.47% for girls respectively. The susceptibility of various physical measures in reflecting children's nutrition status was analysed, and the practical use of these discriminant functions was discussed.

Adolescent↗
PubMed1992

Prediction of various grades of cervical neoplasia on plastic-embedded cytobrush samples. Discriminant analysis with qualitative and quantitative predictors.

The purpose of this study was to investigate whether discrimination into five groups of various grades of cervical preneoplasia and neoplasia is possible using discriminant analysis models. Data were analyzed for 242 cases diagnosed as either slight dysplasia (n = 50), moderate dysplasia (n = 50), severe dysplasia (n = 50), carcinoma in situ (n = 50) or invasive carcinoma (n = 42) and consisted of qualitative and quantitative features of cells derived from a repeat sample taken from the ectocervix as well as the endocervix using Cytobrushes. The samples were embedded in plastic, and thin sections were prepared, resulting in a monolayer of cut nuclei. The percentages of expected correct prediction were obtained by using 10,000 double cross-validation samples; the mean percentage of correct prediction into five groups using cross-validation was 65% (in the original analysis, 72%) and into two groups (dysplasia versus carcinoma in situ and invasive carcinoma) was 91% (93%). The results reflect group discrimination potential; we do not claim reliability of prediction for an individual patient. The patients were not a representative sample of the population; to investigate whether groups of patients could be discriminated on the basis of both qualitative and quantitative features, the data analyzed contain an almost equal number of observations in each of the five groups. The results indicate that features do not classify the cases in the same way; the discriminant analyses suggest that quantitative features play an important role in the discrimination of dysplasia from carcinoma cases, while the majority of the qualitative features are important in discrimination within the three dysplasia groups.

Biopsy↗
PubMed1986

[Differential diagnosis of jaundice by combined noninvasive diagnostic means using a logistic discriminant analysis].

Using a logistic discriminant function we retrospectively evaluated the diagnostic value of laboratory features and abdominal sonography in 70 patients with jaundice. 18 patients had an extrahepatic obstruction of the common bile duct (EHO), 22 patients had metastatic liver disease (MLD) and 30 patients had an infectious or toxic hepatocellular disease (HCD). The sensitivity resp. specificity of the 5 laboratory values AP, GGT, GPT, GOT and bilirubin was 22% resp. 90% for diagnosing EHO, 82% resp. 79% for diagnosing MLD and 67% resp. 68% for diagnosing HCD. The diagnostic value determined by their Chi2-value was AP greater than GPT greater than bilirubin greater than GOT greater than GGT. An undoubtedly dilated common bile duct was seen in 56% of the patients with EHO and in none of the other patients. Metastatic lesions were seen exclusively in 81% of the patients with MLD. No distinct sonographic sign could be found for the patients with HCD. The combination of AP, GPT and bilirubin with the result of abdominal sonography in a logistic discriminant function led to a correct a posteriori classification of all patients. Using a mathematical classification model jaundiced patients can be diagnosed on the basis of noninvasive methods alone and invasive procedures should be restricted to therapeutic interventions.

Cholestasis, Extrahepatic↗
PubMed1995

Automated grading of astrocytomas based on histomorphometric analysis of Ki-67 and Feulgen stained paraffin sections. Classification results of neuronal networks and discriminant analysis.

In stereotactically obtained astrocytoma biopsies, four morphometric nuclear parameters were determined with the use of an image analysis system. A special Ki-67 (MIB1)/Feulgen stain made it possible to quantify the essential characteristics of gliomas of the astrocytoma/glioblastoma group: growth pattern, cellularity, proliferation tendency and nucleus pleomorphism. A grading scale based on a cluster analysis resembling the WHO-scheme, which is suitable for automated astrocytoma grading, was developed. Large back propagation neural networks were used and their results compared with those of a classical multivariate discriminant classification analysis. It is possible to show that the neural network technology is superior to the statistical approach for automated astrocytoma grading. Based on the results of our study we believe neural network technology to be useful for tumour grading problems. The presented approach can be generalized for the automated grading of other tumour entities.

Astrocytoma↗
PubMed2001

Penalized discriminant analysis of [15O]-water PET brain images with prediction error selection of smoothness and regularization hyperparameters.

We propose a flexible, comprehensive approach for analysis of [15O]-water positron emission tomography (PET) brain images using a penalized version of linear discriminant analysis (PDA). We applied it to scans from 20 subjects (eight scans/subject) performing a finger movement task and analyzed: 1) two classes to obtain a covariance-normalized baseline-activation image, and 2) eight classes for the mean within subject temporal structure which contained baseline-activation and time-dependent changes in a two-dimensional canonical subspace. We imposed spatial smoothness on the resulting image(s) by expanding it in five tensor-product B-spline (TPS) bases of varying smoothness, and further regularized with a ridge-type penalty on the noise covariance matrix. The discrimination approach of PDA provides a probabilistic framework within which prediction error (PE) estimates are derived. We used these to optimize over TPS bases and a ridge hyperparameter (expressed as equivalent degrees of freedom, EDF). We obtained unbiased, low variance PE estimates using modern resampling tools (.632+ Bootstrap and cross validation), and compared PDA of 1) TPS-projected, mean-normalized and unnormalized scans and 2) mean-normalized scans with and without additional presmoothing. By examining the tradeoffs between PE and EDF, as a function of basis selection and image smoothing we demonstrate the utility of PDA, the PE framework, and the relationship between singular value decomposition and smooth TPS bases in the analysis of functional neuroimages.

Brain↗
PubMed1995

Iron deficiency and anemia of chronic disease in elderly women: a discriminant-analysis approach for differentiation.

To differentiate iron-deficiency anemia and anemia associated with chronic inflammatory diseases in elderly women, subsets of laboratory, dietary, and functional assessment variables were obtained by using discriminant analysis. Fifty-one subjects (70-79 y of age) were classified into one of four groups on the basis of the presence of iron deficiency and chronic inflammatory disease. Iron deficiency was defined on the basis of a significant response in hemoglobin concentration after iron supplementation. The discriminating subset of laboratory tests consisted of measures for serum ferritin, plasma transferrin receptors, and erythrocyte sedimentation rate. The discriminant function classified subjects into iron-deficient, anemia of chronic disease, or a category in which the two coexist, with an error rate of 18.6%. The addition of other variables (dietary iron and functional assessment information) did not appreciably improve the classification. The results of these three key laboratory tests may help to identify functional iron deficiency in the presence of chronic inflammation.

Aged↗
PubMed2005

Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis.

The support vector machine (SVM), as a novel type of learning machine, was used to develop a classification model of carcinogenic properties of 148 N-nitroso compounds. The seven descriptors calculated solely from the molecular structures of compounds selected by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The obtained results confirmed the discriminative capacity of the calculated descriptors. The result of SVM (total accuracy of 95.2%) is better than that of LDA (total accuracy of 89.8%).

Artificial Intelligence↗
PubMed1995

Discriminant analysis algorithm based on a distance function and on a Bayesian decision.

We propose a new algorithm for the allocation of an individual to one of several possible groups or populations. The algorithm enables us to define a finite partition over the sample space, based on distance function. This partition is used, jointly with the application of a standard Bayesian decision rule, to allocate individuals to the populations. The algorithm also provides a measure of the allocation confidence for each individual, in a similar manner to that of logistic regression. The error rates for classification are also computed using the leave-one-out method. Results are compared with those obtained with other discriminant analysis techniques previously reported: Fisher's linear discriminant function, the quadratic discriminant function, logistic discrimination, and others.

Adolescent↗
PubMed1993

Reasons given by college students for drinking: a discriminant analysis investigation.

Based on self-reported levels of alcohol consumption, 473 college students (295 female and 178 male) were placed into at-risk or not-at-risk groups. Using reasons given for drinking as the independent variables, discriminant analysis procedures were conducted separately on the males and females to determine if a function could be found which would discriminate between the groups. For the female group, 11 of 22 reasons defined a discriminant function which accounted for 36% of the variance between the groups (p < .001). This function was also able to correctly classify 71% of the holdout sample. For the males, five of the 22 reasons defined a discriminant function which accounted for 36% of the variance between the groups (p < .001). This function was able to correctly classify 69% of the holdout sample.

Adult↗
PubMed1994

Heart rate variability and susceptibility for sudden cardiac death: an example of multivariable optimal discriminant analysis.

The statistical classification problem motivates the search for an analytical procedure capable of classifying observations accurately into one of two or more groups on the basis of information with respect to one or more attributes, and constitutes a fundamental challenge for all scientific disciplines. Although there are many classification methodologies, only optimal discriminant analysis (ODA) explicitly guarantees that the discriminant classifier will maximize classification accuracy in the training sample. This paper presents the first example of multivariable ODA (MultiODA) in medicine, for an application in which we employ three attributes (age and two measures of heart rate variability) to predict susceptibility to sudden cardiac death for a sample of 45 patients. MultiODA outperformed logistic regression analysis on every classification performance index (overall accuracy, sensitivity, specificity, and positive and negative predictive values). In fact, the worst performance result achieved by MultiODA (in total sample or leave-one-out validity analysis) exceeded the best performance achieved by logistic regression analysis. We conclude that ODA offers promise as a methodology capable of improving the classification performance achieved by medical researchers, and that clearly merits investigation in future research.

Arrhythmias, Cardiac↗
PubMedDate not supplied

"Etiological characterization of hirsutism by means of a chemometric technique: the linear discriminant analysis".

This preliminary study concerns the evaluation of a chemometric technique, the so called Linear Discriminant Analysis (LDA) for an adequate nosological characterization of the more common forms of hirsutism: i.e., the Micropolycystic Ovary Syndrome (MPCO) and the Idiopathic Hirsutism (I.H.). The obtained data and, particularly, the evidence of statistically significant values of the Linear Discriminant Function (mean = 12.7; p less than 0.02), clearly show the effectiveness of LDA as a practical and suitable method for a more proper detection and classification of MPCO and I.H.

Adolescent↗
PubMedDate not supplied

Construct validity of the In-Hand Manipulation Test: a discriminant analysis with children without disability and children with spastic diplegia.

OBJECTIVE: This study examined the construct validity of the In-Hand Manipulation Test (IMT) by assessing the test's ability to discriminate between samples of children with and without known fine motor problems. METHOD: The IMT was administered to 55 children without known fine motor problems and 24 children with spastic diplegia who had mild to moderate fine motor problems. Construct validity was estimated by evaluating how accurately the IMT classified the children as having or not having fine motor problems on the basis of total score. RESULTS: A discriminant analysis indicated that IMT total score correctly classified 83.33% of the participants as having or not having fine motor problems. CONCLUSION: The IMT has adequate construct validity to classify the participants of this study and for continued use as a research instrument to assess children's in-hand manipulation skills. Additional validity studies of the IMT are needed with other samples of children before its use for clinical purposes.

Case-Control Studies↗
PubMed1998

Predicting hospitalisation of patients with diabetes mellitus. An application of the Bayesian discriminant analysis.

The objective of this study was to develop, and subsequently test, a Bayesian discrimination model for the purpose of identifying both the personal and the healthcare system characteristics predictive of hospitalisation for the treatment of patients with diabetes mellitus or commonly observed cormorbidities associated with the disease. First, a Bayesian classification framework was proposed. The model was then tested by using a logit regression technique in order to estimate the probability of one or more hospitalisation events among patients with diabetes. The study used claims data extracted from the Hawaii Medical Service Association (HMSA) Private Business Claims (PBS) files for the 1995 calendar year. Patients under 65 years were identified by paid claims with ICD-9-CM diagnosis codes of 250.xx which gave a sample size of 6841 patients. Age, gender, various pharmacotherapy variables, presence of hypertension, hyperlipidaemia, congestive heart failure, multiple cardiovascular diseases, any combination of commonly observed comorbidities, dialysis services and annual eye examination are highly predictive of 1 or more hospitalisation events. The model shows a predictive power of almost 90%. This study found that multivariate discriminant analysis using a logit regression model successfully identifies: (i) important explanatory variables predictive of hospitalisation; (ii) assigns patients into 1 of 2 mutually exclusive classes; and (iii) offers a benchmark for a comprehensive disease management strategy for patients with more complicated diabetes.

Bayes Theorem↗
PubMed2001

Locating the genes underlying a simulated complex disease by discriminant analysis.

The purpose of this study was to propose and evaluate a novel multivariate approach for genetic mapping of complex binary human diseases. This approach uses the application of either of two methods of standard (stepwise) discriminant analysis to detect linkage based on the differential marker identity-by-descent distributions among the three affection groups of sib pairs (concordantly affected, discordant, and concordantly unaffected). One of the advantages of this approach is that it allows for simultaneously testing all markers, as well as other genetic and environmental factors, in a single multivariate setting. We have explored its properties and behaviors via an application to the simulated data in Genetic Analysis Workshop 12.

Chromosome Mapping↗
PubMed1975

Diagnosis of liver diseases by laboratory results and discriminant analysis. Identification of best combinations of laboratory tests.

Patients with different liver diseases were studied by discriminant analysis. Groups of patients classified mainly on the basis of liver biopsy findings showed functional differences which permitted a consistent reclassification by discriminant functions using laboratory results. Optimal combinations of laboratory tests for the separation of liver diseases were defined. Different combinations were found, dependent on the subsets of liver diseases studied.

Acute Disease↗
PubMed1993

Computer-based prediction of psychotropic drug classes based on a discriminant analysis of drug effects on rat sleep.

The goal of the present study was to classify psychotropic drugs on the basis of EEG-defined rat sleep-waking behaviour. Using an automated sleep classification system it was found that some of the drug-induced changes in sleep-waking behaviour were specific for the pharmacotherapeutic treatment class to which the drug belonged. In several preliminary experiments we further found that drugs may have effects on rat EEG independent of their effects on rat sleep-waking behaviour and that these pharmaco-EEG effects may be different for the various sleep and waking stages. By analysing sleep class-independent EEG-spectral parameters a single drug effect score can, moreover, be obtained giving information on drug pharmacodynamics. The drug-induced changes in sleep-waking behaviour were used to classify a large number of drugs into several therapy classes by means of a discriminant analysis procedure. Antidepressants, antipsychotics and stimulants were discriminated successfully from each other and from placebo by this system, whereas nootropics classified as placebo. Anxiolytics, hypnotics and anticonvulsants classified poorly. Their classification is hampered by the lack of specific compounds. Assigned drug class and assignment probability were dose dependent. In the discussion of the present study it is suggested that animal pharmaco-sleep and pharmaco-EEG studies are not mutually exclusive approaches, but that they may complement each other.

Animals↗