6Machine Learning–Based Disease Diagnosis and Prediction for E-Healthcare System
Shruti Suhas Kute1, Shreyas Madhav A. V.1*, Shabnam Kumari2 and Aswathy S. U.3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
2SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
3Department of Computer Science and Engineering, Jyothi Engineering College, Cheruthuruthy, Thrissur, Kerala, India
Abstract
The rapid development of IoT and AI technologies over the past decade has resulted in the rise of several medical applications for digitizing the healthcare sector. One important domain within healthcare is that has yielded maximal benefits due to the integration of emerging technologies in medical diagnostics. The diagnosis of a disease requires an in-depth analysis of a patient’s symptoms, genetic history, environmental conditions, and so on. In a traditional healthcare system, doctors arrive at informed conclusions by studying the data presented to them by the patients and their health records. The advent of IoT and deep learning has caused a metamorphosis of conventional medical diagnostics into automated decision-making systems powered by machine learning. The requirements of doctor’s intervention at every step are minimized and consultations are made after initial automated screening tests. Several machine learning frameworks have been established in the past for the diagnosis of a wide collection of diseases including ...
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