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September 16, 2025Applied SciencesOpen Access

Enhancing Diabetes Diagnosis Through Machine Learning: A Comparative Study

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Authors

DEDenisse Enríquez-OrtegaBCBryan Chulde-FernándezPPPaloma Planells del Pozo

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Overview

This analysis finds that Random Forest and Decision Tree models enhance diabetes diagnosis, suggesting effective diagnostic alternatives.

Key Points

  • The Random Forest and Decision Tree models achieved accuracy rates of 98.15% and 97.51%, respectively.
  • Machine learning models, especially ensemble-based ones like Random Forest, show remarkable potential for diabetes prediction.
  • Data preprocessing techniques such as normalization and class balancing significantly improve model performance.
  • Integrating machine learning into clinical decision-making can provide cost-effective alternatives to conventional methods.

Cite This Study

Enríquez-Ortega et al. (2025) studied this question.

synapsesocial.com/papers/68d42336713b0b5dfea6b979https://doi.org/10.3390/app151810087
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Advanced supervised machine learning methods for precise diabetes mellitus prediction using feature selection2025 · 9 citations
  2. 2Diabetes Disease Prediction Using Machine Learning Classification Algorithms2025
  3. 3Data to Diagnosis: Evaluating Machine Learning Algorithms for Predictive Healthcare in Diabetes2025
  4. 4Comparative Study of Machine Learning Techniques for Diabetes Forecasting2025
  5. 5Diabetes Risk Prediction Model Using Machine Learning2025 · 2 citations