International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 July-August 2024 Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
July-August 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

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
View / Download PDF File

Share this


E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2 Cover Page Vol 6 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com