Artificial intelligence–based neural network for the diagnosis of diabetes and COVID: ANN model with optimum predictor variable

Gilbert Roland; Navin Kumar; Bharathi Gururaj; Richa Richa; Sunil Devidas Bobade; Melanie Elizabeth Lourens

doi:10.53730/ijhs.v6nS1.8606

Authors

Gilbert Roland Department of Health Studies, Astrialearning Consortium of Universities, 100 S. Ashley Drive Suite 600, Tampa, FL 33602 USA
Navin Kumar
drnavinkr1998@gmail.com
Associate professor, Department of community medicine, Narayan medical college and hospital Sasaram, Bihar
Bharathi Gururaj Associate Professor, Department of Electronics and Communication Engineering ACS college of Engineering, Bangalore 560074
Richa Principal, Government Leather Institute, Uttar Pradesh, India
Sunil Devidas Bobade New Horizon Institute of Technology and Management, Thane, India 400615
Melanie Elizabeth Lourens Deputy Dean: Faculty of Management Sciences, Durban University of Technology City, Durban, KwaZulu-Natal, South Africa

Keywords:

Diabetes Mellitus, Neural Network, Artificial Intelligence

Abstract

In many nations, the prevalence of diabetes is rising, and its impact on national health cannot be overlooked. Smart medicine is a medical concept in which technology is used to aid in disease detection and treatment. The objective of this study is to take a gander at the information and look at changed diabetic mellitus forecasting algorithms. According to rising dismalness as of late, the quantity of diabetic patients worldwide will arrive at 642 million out of 2040, suggesting that one out of each 10persons would be affected. This worrisome figure, without a question, demands immediate attention. AI has been applied to an assortment of aspects of clinical wellbeing as a result of its rapid progress. To predict diabetes mellitus in this review, we utilized a choice tree, an arbitrary timberland, and a neural organization.

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