Machine learning with Internet of Things data for risk prediction: Application in ESRD

Connected objects are the key for many intelligent systems for instance, direct access to physical and physiological values and collecting information about the human body. Our research works aim to develop non-invasive methods that predict risk for dialysis patient in End-Stage Renal Disease (ESRD)...

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Bibliographic Details
Published inProceedings of the ... International Conference on Research Challenges in Information Science pp. 1 - 6
Main Authors Fki, Zeineb, Ammar, Boudour, Ayed, Mounir Ben
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2018
Subjects
Biomarkers
Biosensors
Cloud computing
Diseases
Machine learning
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ISSN2151-1357
DOI10.1109/RCIS.2018.8406669

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Summary:Connected objects are the key for many intelligent systems for instance, direct access to physical and physiological values and collecting information about the human body. Our research works aim to develop non-invasive methods that predict risk for dialysis patient in End-Stage Renal Disease (ESRD) at a smart home care system based on Internet of Things (IoT). However, the IoT components pose many new challenges in collecting more fine grained information called biomarkers. In this paper, we describe our work in progress to predict dialysis biomarkers from IoT sensors. To address this problem, we present our ongoing research to develop a modern data analytics environment using machine learning techniques. This paper gives also an overview about literature review and discusses open issues.
ISSN:2151-1357
DOI:10.1109/RCIS.2018.8406669