A Comparison of Random Forest-Based Methods for Racial/Ethnic-Specific Classification of Obesity
نویسندگان
چکیده
A Comparison of Random Forest-Based Methods for Racial/Ethnic-Specific Classification of Obesity
منابع مشابه
Propensity based classification: Dehalogenase and non-dehalogenase enzymes
The present work was designed to classify and differentiate between the dehalogenase enzyme to non–dehalogenases (other hydrolases) by taking the amino acid propensity at the core, surface and both the parts. The data sets were made on an individual basis by selecting the 3D structures of protein available in the PDB (Protein Data Bank). The prediction of the core amino acid were predicted by I...
متن کاملRIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning
Anonymized electronic medical records are an increasingly popular source of research data. However, these datasets often lack race and ethnicity information. This creates problems for researchers modeling human disease, as race and ethnicity are powerful confounders for many health exposures and treatment outcomes; race and ethnicity are closely linked to population-specific genetic variation. ...
متن کاملForest Stand Types Classification Using Tree-Based Algorithms and SPOT-HRG Data
Forest types mapping, is one of the most necessary elements in the forest management and silviculture treatments. Traditional methods such as field surveys are almost time-consuming and cost-intensive. Improvements in remote sensing data sources and classification –estimation methods are preparing new opportunities for obtaining more accurate forest biophysical attributes maps. This research co...
متن کاملSemi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk
This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...
متن کاملAuthor gender identification from text using Bayesian Random Forest
Nowadays high usage of users from virtual environments and their connection via social networks like Facebook, Instagram, and Twitter shows the necessity of finding out shared subjects in this environment more than before. There are several applications that benefit from reliable methods for inferring age and gender of users in social media. Such applications exist across a wide area of fields,...
متن کامل