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Machine Learning Algorithms and Wireless Sensor Network Applied to Medical Diagnosis: A Systematic Review
American Journal of Electromagnetics and Applications
Volume 7, Issue 2, December 2019, Pages: 25-33
Received: Nov. 18, 2019; Accepted: Nov. 27, 2019; Published: Dec. 17, 2019
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Ibrahim Goni, Department of Computer Science, Faculty of Science, Adamawa State University, Mubi, Nigeria
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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.
Computational Intelligence, Artificial Intelligence, Soft Computing, Wireless Sensor Network
To cite this article
Ibrahim Goni, Machine Learning Algorithms and Wireless Sensor Network Applied to Medical Diagnosis: A Systematic Review, American Journal of Electromagnetics and Applications. Vol. 7, No. 2, 2019, pp. 25-33. doi: 10.11648/j.ajea.20190702.13
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