New techniques for the prediction of tumour behaviour are needed since statistical analysis has a poor accuracy and is not applicable to the individual. Artificial Intelligence (AI) may provide these suitable methods. We have compared the predictive accuracies of neuro-fuzzy modelling (NFM), artificial neural networks (ANN) and traditional statistical methods, for the behaviour of bladder cance...

OBJECTIVES In this paper three statistical methods [logistic regression, classification and regression tree (CART), and fuzzy inference] for the prediction of lymph node metastasis in carcinoma of the tongue are compared. METHODS A retrospective collection of data in 75 patients treated for tongue cancer was carried out at the Clinic and Policlinic for Oral and Maxillo-facial Surgery at the U...

the aim of this study was to investigate the potential association between growth hormone gh/alui and growth hormone receptor ghr/alui polymorphisms with milk yield and reproductive performances in holstein dairy cows in iran. blood samples of 150 holstein cows were collected and their genomic dna was extracted using gene-fanavaran dna extracting kit. fragments of the 428 bp of exon 5 growth ho...

Logistic regression is a technique to map the input feature to the posterior probability for a binary class. The optimal parameter of regression function is obtained by maximizing log likelihood of training data. In this report, we implement two optimization techniques 1) stochastic gradient decent (SGD); 2) limited-memory BroydenFletcherGoldfarbShanno (L-BFGS) to optimize the log likelihood fu...

In this paper, the fuzzy logic and trapezoidal intuitionistic number were presented, as well some properties of semi- parametric logistic regression model when using number. The output variable represents dependent sometimes cannot be determined in only two cases (response, non-response)or (success, failure) more than responses, especially medical studies; therefore so, use a semi with (depende...

Diabetes is a chronic health condition that impairs the body's ability to convert food energy, recognized by persistently high levels of blood glucose. Undiagnosed diabetes can cause many complications, including retinopathy, nephropathy, neuropathy, and other vascular disorders. Machine learning methods be very useful for disease identification, prediction, treatment. This paper proposes new e...

Multiple Logistic Regression Just as in OLS regression, logistic models can include more than one predictor. The analysis options are similar to regression. One can choose to select variables, as with a stepwise procedure, or one can enter the predictors simultaneously, or they can be entered in blocks. Variations of the likelihood ratio test can be conducted in which the chi-square test (G) is...

Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of f...

OBJECTIVE To develop and compare two mathematical models, the first one based on logistic regression and the second one on fuzzy sets theory, aiming at defining a laboratory testing-based measure of indication for submitting patients to parathyroid scintigraphy. METHODS One-hundred and ninety-four patients with serum calcium and parathyroid hormone available were identified from the data regi...

MOTIVATION Interpretation of classification models derived from gene-expression data is usually not simple, yet it is an important aspect in the analytical process. We investigate the performance of small rule-based classifiers based on fuzzy logic in five datasets that are different in size, laboratory origin and biomedical domain. RESULTS The classifiers resulted in rules that can be readil...