Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis
نویسندگان
چکیده
منابع مشابه
Comparison of ordinary logistic regression and robust logistic regression models in modeling of pre-diabetes risk factors
Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over ...
متن کاملcomparison between conventional and alternative technique in extraction of maxillary molars
چکیده ندارد.
15 صفحه اولThe Comparison of Credit Risk between Artificial Neural Network and Logistic Regression Models in Tose-Taavon Bank in Guilan
One of the most important issues always facing banks and financial institutes is the issue of credit risk or the possibility of failure in the fulfillment of obligations by applicants who are receiving credit facilities. The considerable number of banks’ delayed loan payments all around the world shows the importance of this issue and the necessary consideration of this topic. Accordingly...
متن کاملMortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.
INTRODUCTION Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. METHODS An established...
متن کاملComparison of Gestational Diabetes Prediction Between Logistic Regression, Discriminant Analysis, Decision Tree and Artificial Neural Network Models
Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2019
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0221911