International Journal For Multidisciplinary Research

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Machine Learning-based Diabetes Classification using Raspberry-Pi

Author(s) Maha Lakshmi B, Srinivasa Rao G, Tejaswini R, Supraja K, Ramya A, Sumythri V
Country India
Abstract Diabetes is one of the diseases that is spreading get along faster among people. Typically, determining the level of diabetes in patients takes time. To reduce this problem, machine learning algorithms are said to help predict and classify type 1 or type 2 diabetes. Then, a liquid crystal display (LCD) is said to be used to show the diabetes reading and communicated via the Internet of Things (IoT). alert and send messages to doctors and patients regarding diabetes by reading for other drugs. This project is useful for early detection and also presents a hypothetical IoT-based diabetes monitoring system for healthy and sick people to monitor blood glucose (BG) levels. The tools used in this project are Raspberry Pi, a non-contact infrared sensor and machine learning algorithms such as logistic regression, K-Nearest Neighbour, and Support Vector Machine. It has higher precision than other ML algorithms.
Keywords logistic regression, K-nearest neighbour, vector machine support, Raspberry Pi, IR sensor, LCD, IoT (Internet of Things)
Field Engineering
Published In Volume 5, Issue 2, March-April 2023
Published On 2023-04-18
Cite This Machine Learning-based Diabetes Classification using Raspberry-Pi - Maha Lakshmi B, Srinivasa Rao G, Tejaswini R, Supraja K, Ramya A, Sumythri V - IJFMR Volume 5, Issue 2, March-April 2023. DOI 10.36948/ijfmr.2023.v05i02.2461
DOI https://doi.org/10.36948/ijfmr.2023.v05i02.2461
Short DOI https://doi.org/gr5qs9
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