A random forest classifier for lymph diseases

A comprehensive search for expert classification methods in disease diagnosis and prediction

“…In addition, some other classifiers have a better performance compared with previously published results, for example, best‐first, J48, and REPT classifiers in the analysis of TH dataset; RF classifier in the analysis of BW dataset; RS classifier in the analysis of HC dataset; MLP and RBF classifiers in the analysis of HH dataset; RBF, SMO, and RF classifiers in the analysis of SH dataset; RBF and RAF classifiers in the analysis of HE dataset; SMO classifier in the analysis of LY dataset; J48 in the analysis of BC dataset; NBU classifier in the analysis of HS dataset; SC classifier in the analysis of HC dataset; RF classifier in the analysis of AR dataset; RS, STC, and zeroR classifiers in the analysis of PO dataset; and NBU in the analysis of PT dataset (Table ). Furthermore, some combinations of feature extraction and classification methods, hybrid‐based, and evolutionary learning‐based classification methods in the published literature have a better accuracy than the selected classification methods in the analysis of selected healthcare data, such as AISWNB, CFSWNB, GRWNB, MIWNB, ReFWNB, Tree‐WNB, and RMWNB (Wu et al, ), LWNB and AODE (Jiang, Zhang, et al, ), pedagogical, decompositional, SVMs, a combination of HC and pedagogical (Stoean & Stoean, ), PSO, ABC, and GSA (Bahrololoum, Nezamabadi‐Pour, Bahrololoum, & Saeed, ), and RBF‐MS, RBF‐HS, and RBF‐NNTS (Jaganathan & Kuppuchamy, ) in the analysis of BW dataset; a combination of SVM and feature selection methods (Sun et al, ) in the analysis of DE dataset; a combination of PPPCA and SVM (Shah et al, ) in the analysis of HH dataset; CDW‐NN (Paredes & Vidal, ) and ABC (Schiezaro & Pedrini, ) in the analysis of SH dataset; RF (Azar, Elshazly, Hassanien, & Elkorany, ) in the analysis of LY dataset; NPBC (Soria, Garibaldi, Ambrogi, Biganzoli, & Ellis, ) in the analysis of HS dataset; SRBC and SRBCBG (Chen et al, ) in the analysis of LC dataset; FND (Rodríguez, García‐Osorio, & Maudes, ), a combination of SVM and feature selection methods (Sun et al, ), and SRBC and SRBCBG (Chen et al, ) in the analysis of AR dataset; and PELM (Rong, Ong, Tan, & Zhu, ) in the analysis of LY dataset (Table ). It is also important to note that in the analysis of some datasets, such as TH, BW, and DE, most of the classification methods have a better performance, whereas for some other datasets, such as PO, LI, and PT, a poor performance is observed.…”

Prediction of standard‐dose brain PET image by using MRI and low‐dose brain [18F]FDG PET images

“…Random forest, an ensemble method, 22 has been proven to be one of the most powerful tools in the machine learning community, and has recently gained significantly more popularity for both classification and regression problems, such as remote sensing image classification, 23,24 medical image segmentation, [25][26][27] human diseases/disorders diagnosis, [28][29][30] facial analysis, 31 and so on. Note that, when applied to the nonlinear regression task, random forest is often called a regression forest (RF).…”

Identifying progression related disease risk modules based on the human subcellular signaling networks

“…97 The PRDRMs had a good classification performance and stability by not only the naive Bayes (Fig. 7) but also by random forest classification methods (Fig.…”