title: Classification of atherosclerotic and non-atherosclerotic individuals using multiclass state vector machine.
abstract: 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.
  
  
  CONCLUSIONS
  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.affiliation: Department of Electronics and Communication, PSR Engineering College, Sivakasi, India.
authors.forename: Paulraj Ranjith
authors.initials: PR
authors.lastname: Kumar
authors.affiliation: Department of Electronics and Communication, PSR Engineering College, Sivakasi, India.
authors.forename: Mohan
authors.initials: M
authors.lastname: Priya
mesh_terms: D017677:Age Distribution; D050197:Atherosclerosis / Q000145:classification* / Q000150:complications / Q000453:epidemiology; D002423:Cause of Death / Q000639:trends; D003324:Coronary Artery Disease / Q000453:epidemiology / Q000209:etiology / Q000401:mortality; D003952:Diagnostic Imaging / Q000379:methods*; D018450:Disease Progression; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D015994:Incidence; D007194:India / Q000453:epidemiology; D008297:Male; D011237:Predictive Value of Tests; D025341:Principal Component Analysis / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D017678:Sex Distribution; D060388:Support Vector Machine
keywords: Atherosclerosis; STULONG dataset; feature selection; multiclass SVM classification; principal component analysis