Comput Methods Programs Biomed - A data mining approach for diagnosis of coronary artery disease.

Tópicos

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Resumo

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.

Resumo Limpo

cardiovascular diseas common one main reason death among major type diseas correct intim diagnosi coronari arteri diseas cad import angiographi accur cad diagnosi method howev mani side effect cost exist studi use sever featur collect data patient appli differ data mine algorithm achiev method high accuraci less side effect cost paper dataset call zalizadeh sani patient featur introduc util sever effect featur also featur creation method propos enrich dataset inform gain confid use determin effect featur cad typic chest pain region rwma age effect one besid creat featur mean inform gain moreov q wave st elev highest confid use data mine method featur creation algorithm accuraci achiev higher known approach literatur

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