Multinomial logistic regression is the extension for the (binary) logistic regression when the categorical dependent outcome has more than two levels. For example, instead of predicting only dead or alive, we may have three groups, namely: dead, lost to follow-up, and alive. In the analysis to follow, a reference group has to be chosen for comparison, the appropriate group would be the alive, i...

Logistic regression with binary and multinomial outcomes is commonly used, and researchers have long searched for an interpretable measure of the strength of a particular logistic model. This article describes the large sample properties of some pseudo-R statistics for assessing the predictive strength of the logistic regression model. We present theoretical results regarding the convergence an...

Logistic regression is an important statistical analysis methods widely used in research fields, including health, business and government. On the other hand preserving data privacy is a crucial aspect in every information system. Many privacy-preserving protocols have been proposed for different statistical techniques, with various data distributions, owners and users. In this paper, we propos...

Logistic regression is a commonly used representation for aggregators in Bayesian belief networks when a child has multiple parents. In this paper we consider extending logistic regression to relational models, where we want to model varying populations and interactions among parents. In this paper, we first examine the representational problems caused by population variation. We show how these...

BACKGROUND The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. METHODS AND RESULTS Using data fro...

The evaluation of landslide susceptibility is great significance in the prevention and management geological hazards. accuracy prediction model based on machine learning significantly higher than that traditional expert knowledge conventional mathematical statistics model. correct reasonable selection non-landslide samples greatly improves reliability regional Focusing problem selecting for eva...

background and aims: diabetes type ii (non-insulin dependent) which is one of the most prevalent diabetes types in the world emerges in people with the age of above 55 and genetic and environmental factors interfere in this disease. the aim of this study was to determine the factors affecting diabetes type ii with generalized mixed linear model. methods: population of this study included 2820 p...

background: venous diseases including varicose veins and chronic venous insufficiency are one of the most important pathogenic factors worldwide. high prevalence of varicose veins and its complications is an emerging problem in the twenty-first century. this study aimed to determine the prevalence and associated risk factors of varicose veins in female hairdressers in shahroud, north of iran in...

background: liver cancer mortality is high in thailand but utility of related vital statistics is limited due to national vital registration (vr) data being under reported for specific causes of deaths. accurate methodologies and reliable supplementary data are needed to provide worthy national vital statistics. this study aimed to model liver cancer deaths based on verbal autopsy (va) study in...