tailieunhanh - Automatic heart disease prediction using feature selection and data mining technique

This paper presents an automatic Heart Disease (HD) prediction method based on feature selection with data mining techniques using the provided symptoms and clinical information assigned in the patients dataset. Data mining which allows the extraction of hidden knowledges from the data and explores the relationship between attributes, is the promising technique for HD prediction. | Journal of Computer Science and Cybernetics, , (2018), 33–47 DOI: AUTOMATIC HEART DISEASE PREDICTION USING FEATURE SELECTION AND DATA MINING TECHNIQUE LE MINH HUNG1,a , TRAN DINH TOAN1 , TRAN VAN LANG2 1 Information Technology Faculty, Ho Chi Minh City University of Food Industry 2 Institute of Applied Mechanics and Informatics, VAST a hunglm@ Abstract. This paper presents an automatic Heart Disease (HD) prediction method based on feature selection with data mining techniques using the provided symptoms and clinical information assigned in the patients dataset. Data mining which allows the extraction of hidden knowledges from the data and explores the relationship between attributes, is the promising technique for HD prediction. HD symptoms can be effectively learned by the computer to classify HD into different classes. However, the information provided may include redundant and interrelated symptoms. The use of such information may degrade the classification performance. Feature selection is an effective way to remove such noisy information meanwhile improving the learning accuracy and facilitating a better understanding for learning model. In our method, HD attributes are weighted and re-ordered based on their rank and weights assigned by Infinite Latent Feature Selection (ILFS) method. A soft margin linear Support Vector Machine (SVM) is applied to classify a subset of selected attributes into different HD classes. The experiment is performed using UCI Machine Learning Repository Heart Disease public dataset. Experimental results demonstrated the effectiveness of the proposed method for precise HD prediction making, our method gained the best performance with an accuracy of and an AUC of for distinguishing ‘No presence’ HD with ‘Presence’ HD. Keywords. Data mining, Heart Disease Prediction, Feature Selection, Classification. 1. INTRODUCTION Heart disease (HD) is one of the top leading .

• Kỹ thuật lập trình
• Journal of Computer Science and Cybernetics
• Automatic heart disease prediction
• Feature selection and data mining technique
• Automatic Heart Disease
• The patients dataset
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