MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of systemic lupus erythematosus

Rheumatology ◽  
2009 ◽  
Vol 48 (6) ◽  
pp. 626-631 ◽  
Author(s):  
Zhuochun Huang ◽  
Yunying Shi ◽  
Bei Cai ◽  
Lanlan Wang ◽  
Yongkang Wu ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Decision Tree ◽  
Serum Protein ◽  
Lupus Erythematosus ◽  
Magnetic Beads ◽  
Protein Biomarkers ◽  
Systemic Lupus ◽  
Tree Model ◽  
Maldi Tof Ms ◽  
Tof Ms

Discovery of serum protein biomarkers in rheumatoid arthritis using MALDI-TOF-MS combined with magnetic beads

2011 ◽  
Vol 12 (3) ◽  
pp. 145-151 ◽  
Author(s):  
Xiaoxue Zhang ◽  
Zhaolin Yuan ◽  
Bo Shen ◽  
Min Zhu ◽  
Chibo Liu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Serum Protein ◽  
Magnetic Beads ◽  
Protein Biomarkers ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Tof Ms

scholarly journals Specific serum protein biomarkers of rheumatoid arthritis detected by MALDI-TOF-MS combined with magnetic beads

2010 ◽  
Vol 22 (7) ◽  
pp. 611-618 ◽  
Author(s):  
Q. Niu ◽  
Z. Huang ◽  
Y. Shi ◽  
L. Wang ◽  
X. Pan ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Serum Protein ◽  
Magnetic Beads ◽  
Protein Biomarkers ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Specific Serum ◽  
Tof Ms

scholarly journals Early prediction of systemic lupus erythematosus using hybrid K-Means J48 decision tree algorithm

2017 ◽  
Vol 7 (1.3) ◽  
pp. 28
Author(s):  
S. Gomathi ◽  
V. Narayani
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Decision Tree ◽  
Lupus Erythematosus ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Early Prediction ◽  
Decision Tree Algorithm ◽  
Systemic Lupus ◽  
Tree Algorithm ◽  
J48 Decision Tree

The objective of the paper is to propose an enhanced algorithm for the prediction of chronic, autoimmune disease called Systemic Lupus Erythematosus (SLE). The Hybrid K-means J48 Decision Tree algorithm (HKMJDT) has been proposed for the effective and early prediction of the SLE. The reason for combining both the clustering and classification algorithms is to obtain the best accuracy and to predict the disease in the early stage. The performance of algorithms such as Naïve Bayes, decision tree, random forest, J48 and Hoeffding tree has been combined with K-means clustering algorithm and compared in an effort to find the best algorithm for diagnosing SLE disease. The results of the statistical evaluation with the comparative study show that the effectiveness of different classification techniques depends on the nature and intricacy of the dataset used. K-means combined with J48 algorithm shows the best accuracy rate of 82.14% on the pre-processed data. The work-flow has been proposed to show the execution of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document