ISSN2456-3315
Impact Factor10.57
UGC CAREApproved
Low Cost₹599 / $45
CrossRefDOI
ESTD2016
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH
Authors
A. Poompavai , A. Poongothai
Keywords
Keywords: Diabetes, Explanatory Data Analysis (EDA), Logit Regression, Area Under ROC Curve (AUC), Confusion Matrix (CM), Model Statistics.
Abstract
Artificial Intelligence and Machine Learning are increasingly being used in healthcare for early diabetes detection and management. A dataset of 768 records of female patients, each characterized by eight health-related attributes, is used to predict the onset of diabetes. The dataset includes columns such as pregnancy, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, age, and outcome. The dataset is adapted from the National Institute of Diabetes and Digestive and Kidney diabetes outcome, helping detect gestational diabetes, blood pressure, and diabetes, which can lead to Diseases (NIDDK) and is widely used in machine learning research on healthcare and medical diagnostics. The dataset uses bar graphs and histograms to visually represent categorical variables of serious health issues. The logit regression model predicts significant differences between outcomes and Skin Thickness, Insulin, and Age, but no significant differences between outcomes and Intercept, Pregnancies, Glucose, BP, BMI, and Diabetes Pedigree Function. The binary logistic model has an AUC of 0.84, accuracy of 75.78%, no information rate of 0.8203, kappa-value of 0.43, sensitivity of 0.8913, specificity of 0.7286, precision of 0.4184, recall of 0.8913, and F1 Score of 0.5694.
Full Paper, Downloads & Preview
How To Cite
Choose the style your journal or department asks for, then copy it. Every version below is generated from this paper's own record.
IJRTI — journal style
"PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 1, page no.a410-a415, January-2025, Available :https://ijrti.org/papers/IJRTI2501052.pdf
APA — 7th edition
Poompavai, A., & Poongothai, A. (2025). PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH. International Journal for Research Trends and Innovation, 10(1), a410-a415. https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052
MLA — 9th edition
Poompavai, A., and A. Poongothai. "PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH." International Journal for Research Trends and Innovation, vol. 10, no. 1, 2025, pp. a410-a415, https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052.
Chicago — 17th, bibliography
Poompavai, A., and A. Poongothai. "PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH." International Journal for Research Trends and Innovation 10, no. 1 (2025): a410-a415. https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052.
Harvard — author–date
Poompavai, A. and Poongothai, A. (2025) 'PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH', International Journal for Research Trends and Innovation, 10(1), pp. a410-a415. Available at: https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052
IEEE — numbered reference
A. Poompavai and A. Poongothai, "PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH," IJRTI, vol. 10, no. 1, pp. a410-a415, Jan. 2025.
Vancouver — biomedical
Poompavai A, Poongothai A. PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH. IJRTI. 2025 Jan;10(1):a410-a415.
AMA — 11th edition
Poompavai A, Poongothai A. PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH. IJRTI. 2025;10(1):a410-a415. Accessed at https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2501052,
author = {A. Poompavai and A. Poongothai},
title = {PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH},
journal = {International Journal for Research Trends and Innovation},
volume = {10},
number = {1},
pages = {a410-a415},
year = {2025},
month = {January},
issn = {2456-3315},
url = {https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Poompavai, A.
AU - Poongothai, A.
TI - PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 10
IS - 1
PY - 2025
SN - 2456-3315
UR - https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052
SP - a410-a415
ER -
Issue
Volume 10 Issue 1,
January-2025
Pages : a410-a415
Other Publication Details
Paper Reg. ID
IJRTI_200350
Published Paper ID
IJRTI2501052
Downloads
205,596
Research Area
Science
Country
Chennai, kancheepuram, Tamil Nadu, India
Published
January 2025
Published Paper PDF
https://ijrti.org/papers/IJRTI2501052.pdf
Published Paper URL
https://ijrti.org/viewpaperforall.php?paper=IJRTI2501052
About Publisher
International Journal for Research Trends and Innovation
Published by IJRTI (JW Publication)
2456-3315
ISSN
10.57
Impact Factor
2016
ESTD Year
Open
Access
Impact Factor 10.57 calculated by Google Scholar and Semantic Scholar.
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 10.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
UGC CARE Approved Journal — Transparent Peer-reviewed journal aligned with UGC’s 2025 suggestive parameters, with CrossRef DOI registration and Scopus Standard metadata on every published paper.
Licence
© 2025 — Authors hold the copyright of this article.
This work is licensed under a
Creative Commons Attribution 4.0 International License
and The Open Definition.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0),
which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Disclaimer:
The content, data and findings in this article are based on the authors’ research and have been
peer-reviewed for academic purposes only. Readers are advised to verify all information before practical
or commercial use. The journal and its editorial board are not liable for any errors, losses or
consequences arising from its use.
Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
