Related references
Note: Only part of the references are listed.On Cesaro Averages for Weighted Trees in the Random Forest
Hieu Pham et al.
JOURNAL OF CLASSIFICATION (2020)
Logistic regression was as good as machine learning for predicting major chronic diseases
Simon Nusinovici et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2020)
A weighted random survival forest
Lev V. Utkin et al.
KNOWLEDGE-BASED SYSTEMS (2019)
Predicting congenital heart defects: A comparison of three data mining methods
Yanhong Luo et al.
PLOS ONE (2017)
Pre-operative prediction of surgical morbidity in children: Comparison of five statistical models
Jennifer N. Cooper et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2015)
Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification
Quazi Abidur Rahman et al.
IEEE TRANSACTIONS ON NANOBIOSCIENCE (2015)
The use of data mining to assist crop protection decisions on kiwifruit in New Zealand
M. G. Hill et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2014)
Automated trading with performance weighted random forests and seasonality
Ash Booth et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes
Peter C. Austin et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2013)
Prevalence of minimal hepatic encephalopathy and quality of life evaluations in hospitalized cirrhotic patients in China
Ji-Yao Wang et al.
WORLD JOURNAL OF GASTROENTEROLOGY (2013)
Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran
Lily Tapak et al.
HEALTHCARE INFORMATICS RESEARCH (2013)
MissForest-non-parametric missing value imputation for mixed-type data
Daniel J. Stekhoven et al.
BIOINFORMATICS (2012)
Data mining for credit card fraud: A comparative study
Siddhartha Bhattacharyya et al.
DECISION SUPPORT SYSTEMS (2011)
AUC-RF: A New Strategy for Genomic Profiling with Random Forest
M. Luz Calle et al.
HUMAN HEREDITY (2011)
Predictors of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt in cirrhotic patients: A systematic review
Ming Bai et al.
JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY (2011)
ON MULTI-CLASS COST-SENSITIVE LEARNING
Zhi-Hua Zhou et al.
COMPUTATIONAL INTELLIGENCE (2010)
A comparison of classification models to identify the Fragile X Syndrome
Rafael Pino-Mejias et al.
JOURNAL OF APPLIED STATISTICS (2008)
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
Maciej A. Mazurowski et al.
NEURAL NETWORKS (2008)
Review article: the burden of hepatic encephalopathy
F. F. Poordad
ALIMENTARY PHARMACOLOGY & THERAPEUTICS (2007)
Bias in random forest variable importance measures: Illustrations, sources and a solution
Carolin Strobl et al.
BMC BIOINFORMATICS (2007)
Prediction of hepatic encephalopathy development in patients with severe acute hepatitis
Y Takikawa et al.
DIGESTIVE DISEASES AND SCIENCES (2006)
Statistics review 14: Logistic regression
V Bewick et al.
CRITICAL CARE (2005)
Optimal cut-point and its corresponding youden index to discriminate individuals using pooled blood samples
EF Schisterman et al.
EPIDEMIOLOGY (2005)
Hepatic encephalopathy: a neuropsychiatric disorder involving multiple neurotransmitter systems
RF Butterworth
CURRENT OPINION IN NEUROLOGY (2000)