IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector

• Published in: Peer-to-peer networking and applications Vol. 13; no. 6; pp. 2123 - 2134
• Main Authors: Muthu, BalaAnand, Sivaparthipan, C. B., Manogaran, Gunasekaran, Sundarasekar, Revathi, Kadry, Seifedine, Shanthini, A., Dasel, Antony
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2020; Springer Nature B.V
• Subjects: Artificial intelligence; Belief networks; Communications Engineering; Computer Communication Networks; Data mining; Engineering; Health care; Information Systems and Communication Service; Internet of Things; Networks; Physicians; Regression analysis; Regularization; Signal,Image and Speech Processing; Support systems; Wearable technology; Regularization _ genome wide association study; Disease prediction scheme; Artificial Intelligent (AI)Generalize approximate Reasoning base Intelligence Control (GARIC); Internet of things (IoT); Boltzmann belief network; Sensor
• ISSN: 1936-6442; 1936-6450
• DOI: 10.1007/s12083-019-00823-2

Humans with good health condition is some more difficult in today’s life, because of changing food habit and environment. So we need awareness about the health condition to the survival. The health-support systems faces significant challenges like lack of adequate medical information, preventable errors, data threat, misdiagnosis, and delayed transmission. To overcome this problem, here we proposed wearable sensor which is connected to Internet of things (IoT) based big data i.e. data mining analysis in healthcare. Moreover, here we design Generalize approximate Reasoning base Intelligence Control (GARIC) with regression rules to gather the information about the patient from the IoT. Finally, Train the data to the Artificial intelligence (AI) with the use of deep learning mechanism Boltzmann belief network. Subsequently Regularization _ Genome wide association study (GWAS) is used to predict the diseases. Thus, if the people has affected by some diseases they will get warning by SMS, emails. Etc., after that they got some treatments and advisory from the doctors.