2020/01/09 by Khaled Fawagreh, Mohamed Medhat Gaber
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare #Data Stream Mining Techniques #Nutritional Studies and Diet
paper · pdf · doi:10.1007/s00607-019-00785-6
crossref issued 2020/01/09 · crossref published 2020/01/09 · crossref published-online 2020/01/09 · openalex publication_date 2020/01/09 · crossref created 2020/01/09 · crossref published-print 2020/05/01 · crossref deposited 2021/01/08 · openalex created_date 2025/10/10 · crossref indexed 2026/07/28 · openalex updated_date 2026/07/31
Abstract In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF , which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases.