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Updated: Jan 27, 2026

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Enhancing classification accuracy in medical datasets using a hybrid distance and cluster refinement-based K-means

Hussein A A Al-Khamees1, Mudatheer M Al-Slivani2, Mayameen S Kadhim3

  • 1Computer Techniques Engineering Department, College of Engineering and Technology, Al-Mustaqbal University, 51001, Babil, Iraq. Hussein.Alkhamees@uomus.edu.iq.

Scientific Reports
|January 25, 2026
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Summary

This study enhances K-Means clustering for medical data analysis by introducing a hybrid distance metric and a refinement step, significantly improving accuracy and cluster quality for better clinical decision-making.

Keywords:
Cluster cohesionDistance metricsK_Means clustering methodMachine learningMedical datasets

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Area of Science:

  • Machine learning
  • Data science
  • Medical informatics

Background:

  • Classic K-Means clustering struggles with medical data due to suboptimal distance metrics and lack of post-assignment refinement.
  • These limitations lead to poor cluster cohesion and misgrouping in medical datasets.
  • Existing methods often fail to fully capture complex medical data structures.

Purpose of the Study:

  • To propose a novel enhanced K-Means clustering framework for medical data analysis.
  • To address K-Means limitations by incorporating a hybrid distance metric and a cluster refinement mechanism.
  • To improve the accuracy, interpretability, and robustness of clustering in healthcare applications.

Main Methods:

  • Developed a hybrid distance approach combining cosine and cityblock (Manhattan) metrics with tunable weights.
  • Implemented a cluster refinement mechanism using Z-score outlier detection for reassigning distant samples.
  • Evaluated the framework on Breast Cancer Wisconsin (BCW) and Heart Disease datasets using multiple performance metrics.

Main Results:

  • The enhanced K-Means achieved high accuracies: 0.9825 for BCW and 0.9000 for Heart Disease.
  • Significantly outperformed traditional Euclidean and cosine-based K-Means methods.
  • Demonstrated substantial improvements in homogeneity scores, indicating enhanced cluster quality and separation.

Conclusions:

  • The proposed hybrid K-Means framework offers a practical and effective enhancement for medical data clustering.
  • The combination of a hybrid distance metric and refinement step leads to superior performance over existing methods.
  • This approach holds significant potential for improving unsupervised learning in medical data analysis and clinical decision-making.