This paper is published in Volume-6, Issue-5, 2020
Area
Computer Science Engineering
Author
Sameer Kulkarni
Co-authors
Gargi Hartalkar, Bhumika Mahajan, Pranav Jawaji
Org/Univ
Independent Researcher, India
Pub. Date
15 October, 2020
Paper ID
V6I5-1339
Publisher
Keywords
Clinical Decision Support System, Machine Learning Algorithms, Wearable Medical Sensors

Citationsacebook

IEEE
Sameer Kulkarni, Gargi Hartalkar, Bhumika Mahajan, Pranav Jawaji. A disease prediction system based on Machine Learning algorithms and ensembles using real-time data gathered through wearable medical sensors, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Sameer Kulkarni, Gargi Hartalkar, Bhumika Mahajan, Pranav Jawaji (2020). A disease prediction system based on Machine Learning algorithms and ensembles using real-time data gathered through wearable medical sensors. International Journal of Advance Research, Ideas and Innovations in Technology, 6(5) www.IJARIIT.com.

MLA
Sameer Kulkarni, Gargi Hartalkar, Bhumika Mahajan, Pranav Jawaji. "A disease prediction system based on Machine Learning algorithms and ensembles using real-time data gathered through wearable medical sensors." International Journal of Advance Research, Ideas and Innovations in Technology 6.5 (2020). www.IJARIIT.com.

Abstract

Even with an Annual expenditure $ 8.2 billion (F.Y. 2018-19), Indian healthcare system is far from being affordable and accessible. While the economic development has been gaining momentum since the last decade, India needs major reforms in existing healthcare system. Technology has an important role to play in streamlining the health infrastructure. A Disease prediction system has been proposed to address various deficiencies and provide affordable, readily-available and cost-effective healthcare. The proposed system can effectively predict life threatening diseases like Diabetes and Heart Disease. The system has been implemented by using Machine Learning Algorithms and enhanced to be more accurate than the existing systems. The system is backed by robust Machine Learning Algorithms and ensembles that will predict presence/absence of the diseases accurately. Accurate analysis of medical data benefits early disease detection, patient care and community services. To overcome the difficulty of incomplete data, the system uses Wearable Medical Sensors to gather Real-time data and make prediction based on the collected data. Presently, we demonstrate the system for two diseases but it can be scaled for tackling more diseases. The Disease Prediction system will not only reduce the burden on existing healthcare and diagnosis system but also provide personalized medication.
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