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International Journal of Computer Techniques Volume 12 Issue 4 | Diabetes Prediction Using Voting Classifier: A Machine Learning Approach

Diabetes Prediction Using Voting Classifier

Diabetes Prediction Using Voting Classifier: A Machine Learning Approach

International Journal of Computer Techniques – Volume 12 Issue 4, July – August 2025

ISSN: 2394-2231 | https://ijctjournal.org

Authors

Vinodhini S – Assistant Professor, IT, Velammal Engineering College, Chennai | vinodhini@velammal.edu.in

Vimala Imogen P – Assistant Professor, IT, Velammal Engineering College, Chennai | vimalaimogenp@gmail.com

Mahalakshmi N – UG Scholar, IT, Velammal Engineering College | mahanagarajanvec@gmail.com

Dhushitha M – UG Scholar, IT, Velammal Engineering College | dhushitha1359@gmail.com

Abstract

This research introduces a machine learning framework for early **diabetes prediction** using a **voting classifier approach**. Logistic Regression is compared against Decision Trees, SVM, and Random Forest using a historical dataset with health attributes including **glucose levels, BMI, blood pressure**, and **family history**. Logistic Regression demonstrated **91% accuracy**, outperforming others and showcasing reliability and computational efficiency for healthcare analytics.

Keywords

Diabetes Prediction, Machine Learning, Logistic Regression, Binary Classification, Medical Diagnosis

Conclusion

The voting classifier-based diabetes prediction system offers a **robust, interpretable**, and highly accurate method for medical diagnostics. Its modular design supports **real-time prediction**, seamless deployment in mobile/web platforms, and scalability across healthcare domains. The model’s performance and usability underline its suitability for early diagnosis in clinics, camps, and telemedicine applications—contributing to improved **preventive care and patient outcomes**.

References

  1. Reddy & Ramakrishnan (2022). E2E-DPV: Ensemble Voting for Diabetes Prediction. Journal of Medical Systems.
  2. Shinde & Kulkarni (2024). Time-Efficient Voting Classifier for Diabetes. IEEE Access.
  3. Sharma et al. (2023). AI-Powered Ensemble Framework. ICAHIC, IEEE.
  4. Nivedha & Prakash (2023). Hybrid Voting Model for Diabetes. ICBDAA, IEEE.
  5. Iqbal et al. (2023). Mobile Ensemble Classifiers. Journal of Healthcare Informatics Research.
  6. Gupta & Katarya (2020). Social Media Surveillance for Diabetes Prediction. Journal of Biomedical Informatics.
  7. Nayak et al. (2023). ML-DPV System for Smart Healthcare. Multimedia Tools and Applications.
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