Handling Imbalanced Data in Medical Records Using Entropy with Minkowski Distance

Medical records are essential for disease detection to help establish a diagnosis. Many issues with imbalanced classification are discovered in many cases of early disease detection and diagnosis using machine learning methods, resulting in decreased accuracy values due to imbalanced data distribution caused by the number of positive patients with less disease than normal individuals. To improve the accuracy of the results, a classification architectural model is proposed through a modified oversampling method (SMOTE) using Minkowski distance and adding entropy as a weight value estimation to figure out the number of samples to be made. The feature selection procedure will adopt the hybrid Particle Swarm Optimisation Grey-Wolf Optimisation approach (PSO GWO). Dataset selection evaluated high, medium, and low data dimensions based on the number of features and the total number of dataset samples. The six classification algorithms were compared using datasets involving diabetes, heart, and breast cancer. The final classification results indicated an average accuracy of 74% for diabetes, 83% for heart, and 96% for breast cancer. The proposed approach successfully solves imbalances in medical record data, outperforming Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), and Random Forest classification approaches.

@article{Astuti2025,
  title     = {Handling Imbalanced Data in Medical Records Using Entropy with Minkowski Distance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Lastri Widya Astuti and Ermatita and Dian Palupi Rini},
  doi       = {10.14569/IJACSA.2025.0160286},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160286}
}