Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này:
Nhan đề: Automatic heart disease prediction using feature selection and data mining technique
Tác giả: Le, Minh Hung
Tran, Dinh Toan
Tran, Van Lang
Từ khoá: Data mining
Heart Disease Prediction
Feature Selection
Năm xuất bản: 2018
Tùng thư/Số báo cáo: Journal of Computer Science and Cybernetics;Vol.34(01) .- P.33–47
Tóm tắt: 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 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 result demonstrated the effectiveness of proposed method for precise HD prediction making, our method gained the best performance with an accuracy of 90,65% and an AUC of 0.96 for distinguishing ‘No presence’ HD with ‘Presence’ HD.
Định danh:
ISSN: 1813-9663
Bộ sưu tập: Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

Các tập tin trong tài liệu này:
Tập tin Mô tả Kích thước Định dạng  
_file_8.44 MBAdobe PDFXem
Your IP:

Khi sử dụng các tài liệu trong Thư viện số phải tuân thủ Luật bản quyền.