Clustering Analysis of Physicians' Performance Evaluation: A Comparison of Feature Selection Strategies to Support Medical Decision-Making

Evaluating physicians' performance is one of the fundamental pillars of improving the quality of healthcare in medical institutions, as it contributes to measuring their ability to provide appropriate treatment, interact effectively with patients, and work within healthcare teams. This study aims to explore the impact of attribute selection on the accuracy of physician clustering using the K-Means algorithm, to improve physician performance assessment. Three datasets containing professional, medical, and administrative attributes were analyzed, such as age, nationality, job title, years of experience, number of operations, and evaluations from various entities. The optimal number of clusters was determined using the Elbow and Silhouette Score methods. The results showed that the original feature set and Lasso features performed best at k = 3, with a clear distinction between clusters. The "three-star" cluster performed well at k = 2 but lost some fine details. It was also shown that attribute selection directly affects the number and accuracy of clusters resulting from clustering, allowing for a clearer classification of physician categories. The study recommends using either original features or Lasso features to achieve more effective clustering, which supports improved recruitment, training, and management decision-making processes in healthcare organizations.

@article{Ghazzawi2025,
  title     = {Clustering Analysis of Physicians' Performance Evaluation: A Comparison of Feature Selection Strategies to Support Medical Decision-Making},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Amani Mustafa Ghazzawi and Alaa Omran Almagrabi and Hanaa Mohammed Namankani},
  doi       = {10.14569/IJACSA.2025.0160469},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160469}
}