TY - GEN
T1 - Diagnosis of coronary artery disease using cost-sensitive algorithms
AU - Alizadehsani, Roohallah
AU - Hosseini, Mohammad Javad
AU - Sani, Zahra Alizadeh
AU - Ghandeharioun, Asma
AU - Boghrati, Reihane
PY - 2012
Y1 - 2012
N2 - One of the main causes of death the world over are cardiovascular diseases, of which coronary artery disease (CAD) is a major type. This disease occurs when the diameter narrowing of one of the left anterior descending, left circumflex, or right coronary arteries is equal to or greater than 50 percent. Angiography is the principal diagnostic modality for the stenosis of heart vessels; however, because of its complications and costs, researchers are looking for alternative methods such as data mining. This study conducts data mining algorithms on the Z-Alizadeh Sani dataset which has been collected from 303 random visitors to Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Center. In this paper, the reason of effectiveness of a preprocessing algorithm on the dataset is investigated. This algorithm which has been merely introduced in our previous works, extracts three new features from the dataset. These features are then used to enrich the primary dataset in order to achieve more accurate results. Moreover, despite the fact that misclassification of diseased patients has more side effects than that of healthy ones, to the best of our knowledge cost-sensitive algorithms have yet to be used in this field. Therefore, in this paper 10-fold cross validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 were employed. As a result, the SMO algorithm has yield to very high sensitivity (97.22%) and accuracy (92.09%) rates, the likes of which have not been reported simultaneously in the existing literature.
AB - One of the main causes of death the world over are cardiovascular diseases, of which coronary artery disease (CAD) is a major type. This disease occurs when the diameter narrowing of one of the left anterior descending, left circumflex, or right coronary arteries is equal to or greater than 50 percent. Angiography is the principal diagnostic modality for the stenosis of heart vessels; however, because of its complications and costs, researchers are looking for alternative methods such as data mining. This study conducts data mining algorithms on the Z-Alizadeh Sani dataset which has been collected from 303 random visitors to Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Center. In this paper, the reason of effectiveness of a preprocessing algorithm on the dataset is investigated. This algorithm which has been merely introduced in our previous works, extracts three new features from the dataset. These features are then used to enrich the primary dataset in order to achieve more accurate results. Moreover, despite the fact that misclassification of diseased patients has more side effects than that of healthy ones, to the best of our knowledge cost-sensitive algorithms have yet to be used in this field. Therefore, in this paper 10-fold cross validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 were employed. As a result, the SMO algorithm has yield to very high sensitivity (97.22%) and accuracy (92.09%) rates, the likes of which have not been reported simultaneously in the existing literature.
KW - C4.5 algorithm
KW - Component
KW - Coronary Artery Disease
KW - Cost Sensitive Algorithms
KW - Data Mining
KW - Feature Extraction
KW - Naïve Bayes algorithm
UR - http://www.scopus.com/inward/record.url?scp=84873135633&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873135633&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2012.29
DO - 10.1109/ICDMW.2012.29
M3 - Conference contribution
AN - SCOPUS:84873135633
SN - 9780769549255
T3 - Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
SP - 9
EP - 16
BT - Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
T2 - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Y2 - 10 December 2012 through 10 December 2012
ER -