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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
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Issue: May 2026
Volume 11 | Issue 5
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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
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| Paper Title: | Fingerprint Based Diabetes Risk Prediction System Using Machine Learning |
| Authors Name: | Archana , Abina B.R , Jeni D , Mini Mol M.V |
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IJRTI_211930
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| 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 |
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| 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 |
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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 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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