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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: October 2025
Volume 10 | Issue 10
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 113
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| Paper Title: | PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH |
| Authors Name: | A. Poompavai , A. Poongothai |
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IJRTI_200350
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| Published Paper Id: | IJRTI2501052 |
| Published In: | Volume 10 Issue 1, January-2025 |
| DOI: | |
| 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. |
| Keywords: | Keywords: Diabetes, Explanatory Data Analysis (EDA), Logit Regression, Area Under ROC Curve (AUC), Confusion Matrix (CM), Model Statistics. |
| Cite Article: | "PREDICTING DIABETES ONSET IN FEMALE PATIENTS USING MACHINE LEARNING: A LOGISTIC REGRESSION APPROACH", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 1, page no.a410-a415, January-2025, Available :http://www.ijrti.org/papers/IJRTI2501052.pdf |
| Downloads: | 000327 |
| 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: IJRTI2501052
Registration ID:200350
Published In: Volume 10 Issue 1, January-2025
DOI (Digital Object Identifier):
Page No: a410-a415 Country: Chennai, kancheepuram, Tamil Nadu, India Research Area: Science Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2501052 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2501052 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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