In this research work systematic approach were used to conduct a survey on recent contributions of the authors that applied Machine learning algorithms or computational intelligence, Artificial intelligence and soft computing techniques such as Artificial Neural Network, Fuzzy logic Genetic algorithm, Artificial Immune System Swarm intelligence among others or any combination of these techniques Neuro-fuzzy, Adaptive Neuro-fuzzy, Neuro-genetic, fuzzy-genetic, and so on that is soft computing to medical diagnosis and also a systematic review on the application of wireless sensor network, wireless sensor is a veritable embedded system with a wireless communication function, and that is capable to: Collect physical quantities such as heat, humidity, temperature, vibration, radiation, sound, light, movement, etc. Convert them into digital values which are sent as sensed data to a remote processing station or base station (WSN) to medical and health care delivery. However the survey believed that combining wireless sensor network with soft computing techniques, artificial intelligence techniques can perform well in providing health care services compare to one technique because any of these techniques has certain limitations but together perhaps they two or three techniques connected together would reduce error to a minimum level.
| Published in | American Journal of Electromagnetics and Applications (Volume 7, Issue 2) |
| DOI | 10.11648/j.ajea.20190702.13 |
| Page(s) | 25-33 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Computational Intelligence, Artificial Intelligence, Soft Computing, Wireless Sensor Network
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APA Style
Ibrahim Goni. (2019). Machine Learning Algorithms and Wireless Sensor Network Applied to Medical Diagnosis: A Systematic Review. American Journal of Electromagnetics and Applications, 7(2), 25-33. https://doi.org/10.11648/j.ajea.20190702.13
ACS Style
Ibrahim Goni. Machine Learning Algorithms and Wireless Sensor Network Applied to Medical Diagnosis: A Systematic Review. Am. J. Electromagn. Appl. 2019, 7(2), 25-33. doi: 10.11648/j.ajea.20190702.13
AMA Style
Ibrahim Goni. Machine Learning Algorithms and Wireless Sensor Network Applied to Medical Diagnosis: A Systematic Review. Am J Electromagn Appl. 2019;7(2):25-33. doi: 10.11648/j.ajea.20190702.13
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Download@article{10.11648/j.ajea.20190702.13,
author = {Ibrahim Goni},
title = {Machine Learning Algorithms and Wireless Sensor Network Applied to Medical Diagnosis: A Systematic Review},
journal = {American Journal of Electromagnetics and Applications},
volume = {7},
number = {2},
pages = {25-33},
doi = {10.11648/j.ajea.20190702.13},
url = {https://doi.org/10.11648/j.ajea.20190702.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajea.20190702.13},
abstract = {In this research work systematic approach were used to conduct a survey on recent contributions of the authors that applied Machine learning algorithms or computational intelligence, Artificial intelligence and soft computing techniques such as Artificial Neural Network, Fuzzy logic Genetic algorithm, Artificial Immune System Swarm intelligence among others or any combination of these techniques Neuro-fuzzy, Adaptive Neuro-fuzzy, Neuro-genetic, fuzzy-genetic, and so on that is soft computing to medical diagnosis and also a systematic review on the application of wireless sensor network, wireless sensor is a veritable embedded system with a wireless communication function, and that is capable to: Collect physical quantities such as heat, humidity, temperature, vibration, radiation, sound, light, movement, etc. Convert them into digital values which are sent as sensed data to a remote processing station or base station (WSN) to medical and health care delivery. However the survey believed that combining wireless sensor network with soft computing techniques, artificial intelligence techniques can perform well in providing health care services compare to one technique because any of these techniques has certain limitations but together perhaps they two or three techniques connected together would reduce error to a minimum level.},
year = {2019}
}
TY - JOUR T1 - Machine Learning Algorithms and Wireless Sensor Network Applied to Medical Diagnosis: A Systematic Review AU - Ibrahim Goni Y1 - 2019/12/17 PY - 2019 N1 - https://doi.org/10.11648/j.ajea.20190702.13 DO - 10.11648/j.ajea.20190702.13 T2 - American Journal of Electromagnetics and Applications JF - American Journal of Electromagnetics and Applications JO - American Journal of Electromagnetics and Applications SP - 25 EP - 33 PB - Science Publishing Group SN - 2376-5984 UR - https://doi.org/10.11648/j.ajea.20190702.13 AB - In this research work systematic approach were used to conduct a survey on recent contributions of the authors that applied Machine learning algorithms or computational intelligence, Artificial intelligence and soft computing techniques such as Artificial Neural Network, Fuzzy logic Genetic algorithm, Artificial Immune System Swarm intelligence among others or any combination of these techniques Neuro-fuzzy, Adaptive Neuro-fuzzy, Neuro-genetic, fuzzy-genetic, and so on that is soft computing to medical diagnosis and also a systematic review on the application of wireless sensor network, wireless sensor is a veritable embedded system with a wireless communication function, and that is capable to: Collect physical quantities such as heat, humidity, temperature, vibration, radiation, sound, light, movement, etc. Convert them into digital values which are sent as sensed data to a remote processing station or base station (WSN) to medical and health care delivery. However the survey believed that combining wireless sensor network with soft computing techniques, artificial intelligence techniques can perform well in providing health care services compare to one technique because any of these techniques has certain limitations but together perhaps they two or three techniques connected together would reduce error to a minimum level. VL - 7 IS - 2 ER -
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