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

Kashif Naseer Qureshi, Sadia Din, Gwanggil Jeon, Francesco Piccialli

Research output: Contribution to journalArticlepeer-review

Abstract

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 Android-based 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.

Original languageEnglish
Pages (from-to)486-502
Number of pages17
JournalInformation Sciences
Volume538
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes

Keywords

  • Accuracy
  • Classification
  • Decision tree
  • M-Health
  • Machine learning
  • Models
  • Predictive
  • SVM

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