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A comparative study of machine learning techniques used in non-clinical systems for continuous healthcare of independent livings
- Publication Year :
- 2020
- Publisher :
- arXiv, 2020.
-
Abstract
- New technologies are adapted to made progress in healthcare especially for independent livings. Medication at distance is leading to integrate technologies with medical. Machine learning methods in collaboration with wearable sensor network technology are used to find hidden patterns in data, detect patient movements, observe habits of patient, analyze clinical data of patient, find intention of patients and make decision on the bases of gathered data. This research performs comparative study on non-clinical systems in healthcare for independent livings. In this study, these systems are sub-divided w.r.t their working into two types: single purpose systems and multi-purpose systems. Systems that are built for single specific purpose (e.g. detect fall, detect emergent state of chronic disease patient) and cannot support healthcare generically are known as single purpose systems, where multi-purpose systems are built to serve for multiple problems (e.g. heart attack etc.) by using single system. This study analyzes usages of machine learning techniques in healthcare systems for independent livings. Answer Set Programming (ASP), Artificial Neural Networks, Classification, Sampling and Rule Based Reasoning etc. are some state of art techniques used to determine emergent situations and observe changes in patient data. Among all methods, ASP logic is used most widely, it is due to its feature to deal with incomplete data. It is also observed that system using ANN shows better accuracy than other systems. It is observed that most of the systems created are for single purpose. In this work, 10 single purpose systems and 5 multi-purpose systems are studied. There is need to create more generic systems that can be used for patients with multiple diseases. Also most of the systems created are prototypical. There is need to create systems that can serve healthcare services in real world.<br />Comment: 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)
- Subjects :
- FOS: Computer and information sciences
Artificial neural network
Computer science
business.industry
Emerging technologies
Wearable computer
Rule-based system
Machine learning
computer.software_genre
Answer set programming
Computer Science - Computers and Society
Computers and Society (cs.CY)
Feature (machine learning)
State (computer science)
Artificial intelligence
business
Wireless sensor network
computer
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....72304f135807f19db8af4048993b8758
- Full Text :
- https://doi.org/10.48550/arxiv.2005.09502