Cloud Based Predictive Model for Airborne Disease Based Healthcare Data
S John Joseph1, S Godfrey Winster2
1S.John Joseph, Assistant Professor, Sudharsan Engineering College, Pudukkottai, India.
2S.Godfrey Winster, Professor, Computer Science and Engineering, Saveetha Engineering College, Chennai, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 837-840 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3950129219/2020©BEIESP | DOI: 10.35940/ijeat.B3950.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, the airborne particles have major health impact when it spreads in human, plant and animal beings. Infectious diseases spreads from these particles which are exhaled directly into the air through the exertions of coughing, breathing, talking and sneezing etc. According to the report from World Health Organization (WHO), More than 30 infectious diseases have arrived to harm the health of people in the past years. There’s no medical attention for several infectious diseases to take prevention and remedy. India have lack of healthcare data to take control of the endemic infectious diseases. This paper uses predictive model which is provide a preventive guidance and suggestions for predicted Airborne diseases through machine learning algorithms. Azure machine learning studio is a cloud based environment which provides machine learning algorithmic approaches to make an intelligent model based solution to solve the particular domain based problems. This proposed model will produce an efficient outcome and helps to take better protection from the infectious diseases.
Keywords: Cloud computing, Health care Analytic, Machine Learning, Predictive Analysis, Disease prediction.