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A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity

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@article{IJASEIT12152,
   author = {Riyanto Sigit and Achmad Basuki and - Anwar},
   title = {A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {3},
   year = {2020},
   pages = {964--973},
   keywords = {ultrasound images; left ventricle; optical flow; feature extraction; gradient boosting classifier.},
   abstract = {Echocardiography is a method of examination with high-frequency sound waves to obtain images of heart organs. Examination of heart health conditions with echocardiography as an imaging method, serves to detect the potential for heart disease, thus that the right treatment from the evaluation results can be decided. Examination of the source of heart disease with echocardiography was performed using several views, namely the long axis, short axis, two-chamber, and four-chamber. However, the assessment of cardiac function is still carried out conventionally. Thus it is necessary to build a system that can assess cardiac function. This study proposes a feature extraction method for the classification of heart disease based on the left ventricular motion on the short-axis. In this method, feature extraction uses 24 good features for the process of tracking the movement of the left ventricle with optical flow. Each good feature produces four features, namely direction (negative direction and positive direction) and distance (negative distance and positive distance) from the results of left ventricular tracking and produces 96 attributes for the whole process. The features that have been obtained are then processed using several classification algorithms with validation techniques that are, k-folds, and leave one out. The result is a classification algorithm with a gradient boosting classifier method that has the best accuracy. Gradient boosting classifier produces accuracy values with validation techniques for k-folds 90.98%, and leave one out 93.23%. This shows that the gradient boosting classifier can be relied upon for the classification of heart disease using the proposed feature extraction method. In this study, we developed a new feature extraction method from the results of tracking the heart wall using optical flow. This algorithm can produce feature values from the tracking results that can be used to build a knowledge system for the classification of heart health conditions.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12152},
   doi = {10.18517/ijaseit.10.3.12152}
}

EndNote

%A Sigit, Riyanto
%A Basuki, Achmad
%A Anwar, -
%D 2020
%T A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity
%B 2020
%9 ultrasound images; left ventricle; optical flow; feature extraction; gradient boosting classifier.
%! A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity
%K ultrasound images; left ventricle; optical flow; feature extraction; gradient boosting classifier.
%X Echocardiography is a method of examination with high-frequency sound waves to obtain images of heart organs. Examination of heart health conditions with echocardiography as an imaging method, serves to detect the potential for heart disease, thus that the right treatment from the evaluation results can be decided. Examination of the source of heart disease with echocardiography was performed using several views, namely the long axis, short axis, two-chamber, and four-chamber. However, the assessment of cardiac function is still carried out conventionally. Thus it is necessary to build a system that can assess cardiac function. This study proposes a feature extraction method for the classification of heart disease based on the left ventricular motion on the short-axis. In this method, feature extraction uses 24 good features for the process of tracking the movement of the left ventricle with optical flow. Each good feature produces four features, namely direction (negative direction and positive direction) and distance (negative distance and positive distance) from the results of left ventricular tracking and produces 96 attributes for the whole process. The features that have been obtained are then processed using several classification algorithms with validation techniques that are, k-folds, and leave one out. The result is a classification algorithm with a gradient boosting classifier method that has the best accuracy. Gradient boosting classifier produces accuracy values with validation techniques for k-folds 90.98%, and leave one out 93.23%. This shows that the gradient boosting classifier can be relied upon for the classification of heart disease using the proposed feature extraction method. In this study, we developed a new feature extraction method from the results of tracking the heart wall using optical flow. This algorithm can produce feature values from the tracking results that can be used to build a knowledge system for the classification of heart health conditions.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12152
%R doi:10.18517/ijaseit.10.3.12152
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 3
%@ 2088-5334

IEEE

Riyanto Sigit,Achmad Basuki and - Anwar,"A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 3, pp. 964-973, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.3.12152.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sigit, Riyanto
AU  - Basuki, Achmad
AU  - Anwar, -
PY  - 2020
TI  - A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 3
Y2  - 2020
SP  - 964
EP  - 973
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - ultrasound images; left ventricle; optical flow; feature extraction; gradient boosting classifier.
N2  - Echocardiography is a method of examination with high-frequency sound waves to obtain images of heart organs. Examination of heart health conditions with echocardiography as an imaging method, serves to detect the potential for heart disease, thus that the right treatment from the evaluation results can be decided. Examination of the source of heart disease with echocardiography was performed using several views, namely the long axis, short axis, two-chamber, and four-chamber. However, the assessment of cardiac function is still carried out conventionally. Thus it is necessary to build a system that can assess cardiac function. This study proposes a feature extraction method for the classification of heart disease based on the left ventricular motion on the short-axis. In this method, feature extraction uses 24 good features for the process of tracking the movement of the left ventricle with optical flow. Each good feature produces four features, namely direction (negative direction and positive direction) and distance (negative distance and positive distance) from the results of left ventricular tracking and produces 96 attributes for the whole process. The features that have been obtained are then processed using several classification algorithms with validation techniques that are, k-folds, and leave one out. The result is a classification algorithm with a gradient boosting classifier method that has the best accuracy. Gradient boosting classifier produces accuracy values with validation techniques for k-folds 90.98%, and leave one out 93.23%. This shows that the gradient boosting classifier can be relied upon for the classification of heart disease using the proposed feature extraction method. In this study, we developed a new feature extraction method from the results of tracking the heart wall using optical flow. This algorithm can produce feature values from the tracking results that can be used to build a knowledge system for the classification of heart health conditions.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12152
DO  - 10.18517/ijaseit.10.3.12152

RefWorks

RT Journal Article
ID 12152
A1 Sigit, Riyanto
A1 Basuki, Achmad
A1 Anwar, -
T1 A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 3
YR 2020
SP 964
OP 973
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 ultrasound images; left ventricle; optical flow; feature extraction; gradient boosting classifier.
AB Echocardiography is a method of examination with high-frequency sound waves to obtain images of heart organs. Examination of heart health conditions with echocardiography as an imaging method, serves to detect the potential for heart disease, thus that the right treatment from the evaluation results can be decided. Examination of the source of heart disease with echocardiography was performed using several views, namely the long axis, short axis, two-chamber, and four-chamber. However, the assessment of cardiac function is still carried out conventionally. Thus it is necessary to build a system that can assess cardiac function. This study proposes a feature extraction method for the classification of heart disease based on the left ventricular motion on the short-axis. In this method, feature extraction uses 24 good features for the process of tracking the movement of the left ventricle with optical flow. Each good feature produces four features, namely direction (negative direction and positive direction) and distance (negative distance and positive distance) from the results of left ventricular tracking and produces 96 attributes for the whole process. The features that have been obtained are then processed using several classification algorithms with validation techniques that are, k-folds, and leave one out. The result is a classification algorithm with a gradient boosting classifier method that has the best accuracy. Gradient boosting classifier produces accuracy values with validation techniques for k-folds 90.98%, and leave one out 93.23%. This shows that the gradient boosting classifier can be relied upon for the classification of heart disease using the proposed feature extraction method. In this study, we developed a new feature extraction method from the results of tracking the heart wall using optical flow. This algorithm can produce feature values from the tracking results that can be used to build a knowledge system for the classification of heart health conditions.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12152
DO  - 10.18517/ijaseit.10.3.12152