IJRTI
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.14

Issue per Year : 12

Volume Published : 11

Issue Published : 120

Article Submitted : 24205

Article Published : 9239

Total Authors : 24597

Total Reviewer : 847

Total Countries : 165

Indexing Partner

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: Fingerprint Based Diabetes Risk Prediction System Using Machine Learning
Authors Name: Archana , Abina B.R , Jeni D , Mini Mol M.V
Download E-Certificate: Download
Author Reg. ID:
IJRTI_211930
Published Paper Id: IJRTI2604316
Published In: Volume 11 Issue 4, April-2026
DOI:
Abstract: This project presents a fingerprint-based diabetes risk prediction system using machine learning techniques. Diabetes is a chronic disease that requires early detection for effective management. Traditional diagnostic methods involve invasive procedures such as blood tests, which can be uncomfortable and time-consuming. To address this issue, the proposed system utilizes fingerprint features as a non-invasive alternative for predicting diabetes. The system extracts unique fingerprint characteristics such as ridge patterns, minutiae points, and texture features, which are then used as input for machine learning models. Algorithms such as Support Vector Machine (SVM), Random Forest, or Neural Networks are trained on labeled datasets to classify individuals as diabetic or non-diabetic. The proposed model aims to provide a fast, cost effective, and user-friendly approach for early diabetes detection. A Random Forest Classifier trained on a labeled dataset combines this biometric input with additional parameters like Age, BMI, and Family History to assess risk. levels LOW, MEDIUM, or HIGH. The entire application is implemented using the Flask framework, providing a simple and interactive web-based interface. The proposed fingerprint-based diabetes risk prediction system uses machine learning techniques to analyze fingerprint features along with health parameters to predict diabetes risk levels.
Keywords: Diabetes Prediction, Machine Learning, Fingerprint Analysis, Dermatoglyphics, Healthcare AI, Early Diagnosis
Cite Article: "Fingerprint Based Diabetes Risk Prediction System Using Machine Learning ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c345-c350, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604316.pdf
Downloads: 000205508
ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2604316
Registration ID:211930
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: c345-c350
Country: Kannya kumari, Tamil Nadu, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604316
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604316
Share Article:

Share Facebook Twitter Google+ Pinterest LinkedIn Email Tumblr WhatsApp Google Gmail

Click Here to Download This Article

Article Preview
Click Here to Download This Article
Major Indexing from www.ijrti.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI.ONE
How to Get DOI?

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Join RMS/Earn 300
IJRTI

WhatsApp
Click Here

Indexing Partner
Copyright © 2026 - All Rights Reserved - IJRTI