Document Detail


Classification of atherosclerotic and non-atherosclerotic individual using multiclass state vector machine.
MedLine Citation:
PMID:  24990170     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
BACKGROUND: Coronary artery disease due to atherosclerosis is an epidemic in India. An estimated 1.3 million Indians died from this in 2000. The projected death from coronary artery disease by 2016 is 2.98 million.OBJECTIVE: To build an effective model which assorts the individuals, whether they belong to the normal group, risk group and pathologic group regarding atherosclerosis in real time by doing necessary preprocessing techniques and to compare the performance with other state-of-the-art machine learning techniques.
METHODS: In this work we have employed STULONG dataset. We have made a deep case study in selecting the attributes which contributes for higher accuracy in predicting the target. The selected attributes includes missing values. Initially our work includes imputation of missing values using Iterative Principal Component Analysis (IPCA). The second step includes selecting best features using Fast Correlation Based Filter (FCBF). Finally the classifier Multiclass Support Vector Machine (SVM) with kernel Radial Basis Function (RBF) is used for classification of atherosclerotic community.
RESULTS: For the subjects belonging to the classes of normal, risk and pathologic, our methodology has outperformed with an accuracy of 99.85%, 99.80% and 99.46% respectively.
CONCLUSION: The combined optimization methods such as Iterative Principal Component Analysis (IPCA) for missing value imputation, Multiclass SVM for classifying normal, risk and pathologic community in real time has performed with overall accuracy of about 98.97%. The essential pre-processing technique, Fast Correlation Based Filter (FCBF) was employed to further intensifying the target.
Authors:
Paulraj Ranjith Kumar; C Balasubramanian; Mohan Priya
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-2
Journal Detail:
Title:  Technology and health care : official journal of the European Society for Engineering and Medicine     Volume:  -     ISSN:  1878-7401     ISO Abbreviation:  Technol Health Care     Publication Date:  2014 Jul 
Date Detail:
Created Date:  2014-7-3     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9314590     Medline TA:  Technol Health Care     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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