Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease

2018 ◽  
Author(s):  
Burak Kolukisa ◽  
Hilal Hacilar ◽  
Gokhan Goy ◽  
Mustafa Kus ◽  
Burcu Bakir-Gungor ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Feature Selection ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Algorithms ◽  
Linear Discriminant ◽  
Artery Disease ◽  
Selection Methodology

scholarly journals Linear discriminant analysis and principal component analysis to predict coronary artery disease

2020 ◽  
Vol 26 (3) ◽  
pp. 2181-2192 ◽  
Author(s):  
Carlo Ricciardi ◽  
Antonio Saverio Valente ◽  
Kyle Edmund ◽  
Valeria Cantoni ◽  
Roberta Green ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Principal Component Analysis ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Discriminant ◽  
Artery Disease

Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.

Linear discriminant analysis of regional ejection fractions in the diagnosis of coronary artery disease

1987 ◽  
Vol 12 (12) ◽  
pp. 602-604 ◽  
Author(s):  
K. H. Douglas ◽  
J. M. Links ◽  
D. C. P. Chen ◽  
D. F. Wong ◽  
Henry N. Wagner
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant ◽  
Artery Disease

scholarly journals Detection of coronary artery disease by reduced features and extreme learning machine

2018 ◽  
Vol 91 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Hidden Node ◽  
Artery Disease ◽  
Learning Machine

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from  a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

scholarly journals Robust and Efficient Linear Discriminant Analysis With L 2,1-Norm for Feature Selection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44100-44110 ◽  
Author(s):  
Libo Yang ◽  
Xuemei Liu ◽  
Feiping Nie ◽  
Yang Liu
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant

Detection of coronary artery disease using multi-modal feature fusion and hybrid feature selection

2020 ◽  
Vol 41 (11) ◽  
pp. 115007
Author(s):  
Huan Zhang ◽  
Xinpei Wang ◽  
Changchun Liu ◽  
Yuanyuan Liu ◽  
Peng Li ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Feature Selection ◽  
Coronary Artery ◽  
Feature Fusion ◽  
Artery Disease
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