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A. Pravin
Sathyabama Institute of Science and Technology
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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A comprehensive study on disease risk predictions in machine learning G. Saranya; A. Pravin
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (343.421 KB) | DOI: 10.11591/ijece.v10i4.pp4217-4225

Abstract

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted.
Co-Authors G. Saranya