An accurate and dynamic predictive model for a smart M-Health system using machine learning

作     者：Qureshi, Kashif Naseer Din, Sadia Jeon, Gwanggil Piccialli, Francesco 

作者机构：Bahria Univ Dept Comp Sci Islamabad Pakistan Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Incheon Natl Univ Dept Embedded Syst Engn Incheon 22012 South Korea Univ Naples Federico II Dept Math & Applicat R Caccioppoli Naples Italy 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2020年第538卷

页      面：486-502页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Research Foundation of Korea(NRF) - Korea government 

主　　题：Machine learning Predictive Models M-Health Classification SVM Decision tree Accuracy 

摘      要：Nowadays, new highly-developed technologies are changing traditional processes related to medical and healthcare systems. Emerging Mobile Health (M-Health) systems are examples of novel technologies based on advanced data communication, deep learning, artificial intelligence, cloud computing, big data, and other machine learning methods. Data are collected from sensor nodes and forwarded to local databases through new technologies that enable cellular networks and then store the information in cloud storage systems. From cloud computing services or medical centres, the data are collected for further analysis. Furthermore, machine learning techniques are being used for accurate prediction of disease analysis and for purposes of classification. This paper presents a detailed overview of M-Health systems, their model and architecture, technologies and applications and also discusses statistical and machine learning approaches. We also propose a secure Androidbased architecture to collect patient data, a reliable cloud-based model for data storage. Finally, a predictive model able to classify cardiovascular diseases according to their seriousness will be discussed. Moreover, the proposed prediction model has been compared with existing models in terms of accuracy, sensitivity, and specificity. The experimental results show encouraging results in terms of the proposed predictive model for an M-Health system. (C) 2020 Elsevier Inc. All rights reserved.