Deep Learning Applications for IoT in Healthcare Using Effects of Mobile Computing
- Authors: Koteswara Rao Vaddempudi1, K.R. Shobha2, Ahmed Mateen Buttar3, Sonu Kumar4, C.R. Aditya5, Ajit Kumar6
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View Affiliations Hide Affiliations1 ECE Department, Prakasam Engineering College, Kandukur, Prakasam, Andhra Pradesh, India 2 Department of Electronics and Telecommunication Engineering, Ramaiah Institute of Technology, MSR Nagar, MSRIT Post, Bangalore, Karnataka, India 560054 3 Department of Computer Science, University of Agriculture Faisalabad, Faisalabad, Punjab, Pakistan-38000 4 Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India-522502 5 Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India-570002 6 School of Computer Science and Engineering, Soongsil University, Seoul, South Korea
- Source: AI and IoT-based intelligent Health Care & Sanitation , pp 33-49
- Publication Date: April 2023
- Language: English
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Diabetes is a chronic ailment characterized by abnormal blood glucose levels. Diabetes is caused by insufficient insulin synthesis or by cells' insensitivity to insulin activity. Glucose is essential to health since it is the primary source of energy for the cells that make up a person's muscles and tissues. On the condition that if a person has diabetes, his or her body either does not create enough insulin or cannot utilize the insulin that is produced. When there isn't enough insulin or cells stop responding to insulin, many dextroses accumulate in the person's vascular framework. As time passes, this could lead to diseases such as kidney disease, vision loss, and coronary disease. Although there is no cure for diabetes, losing weight, eating nutritious foods, being active, and closely monitoring the diabetes level can all assist. In this research, we used Artificial Neural Network to create a Deep Learning (DL) model for predicting Diabetes. Then it was validated using an accuracy of 92%. In addition, with the help of the MIT website, a mobile application was constructed. This project will now assist in predicting the effects of diabetes and deliver personalized warnings. Early detection of pre-diabetes can be extremely beneficial to patients since studies have shown that symptoms of early diabetic difficulties frequently exist at the time of diagnosis.
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