Predicting Disease Risks from Highly Unbalanced Data using Random Forest
نویسندگان
چکیده
Background
منابع مشابه
Comparison of Random Forest and Logistic Regression Methods in Predicting Mortality in Colorectal Cancer Patients and its Related Factors
Background and Objectives: The purpose of this study was to predict the mortality rate of colorectal cancer in Iranian patients and determine the effective factors on the mortality of patients with colorectal cancer using random forest and logistic regression methods. Methods: Data from 304 patients with colorectal cancer registry from the Gastroenterology and Liver Research Center of Shah...
متن کاملPrediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature
MOTIVATION In this work, we aim to develop a computational approach for predicting DNA-binding sites in proteins from amino acid sequences. To avoid overfitting with this method, all available DNA-binding proteins from the Protein Data Bank (PDB) are used to construct the models. The random forest (RF) algorithm is used because it is fast and has robust performance for different parameter value...
متن کاملPredicting disease risks from highly imbalanced data using random forest
BACKGROUND We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. METHODS We employed the National Inpatie...
متن کاملComparison of Random Survival Forests for Competing Risks and Regression Models in Determining Mortality Risk Factors in Breast Cancer Patients in Mahdieh Center, Hamedan, Iran
Introduction: Breast cancer is one of the most common cancers among women worldwide. Patients with cancer may die due to disease progression or other types of events. These different event types are called competing risks. This study aimed to determine the factors affecting the survival of patients with breast cancer using three different approaches: cause-specific hazards regression, subdistri...
متن کاملPredicting Disease Risks Using Feature Selection Based on Random Forest and Support Vector Machine
Disease risk prediction is an important task in biomedicine and bioinformatics. To resolve the problem of high-dimensional features space and highly feature redundancy and to improve the intelligibility of data mining results, a new wrapper method of feature selection based on random forest variables importance measures and support vector machine was proposed. The proposed method combined seque...
متن کامل