Comparative Analysis of Machine Learning Frameworks for Robust Ovarian Cancer Detection Using Feature Selection and Data Balancing

One of the most serious malignancies that affects women’s health worldwide is ovarian cancer. As a result, prompt accurate diagnosis and treatment are necessary. This study’s primary objective is to determine whether or not OC is present within the body of a person by using a range of characteristics gleaned with a couple of health examinations. The article is concentrated on twelve ML techniques used for OC diagnosis. The dataset has been altered by applying the borderline SVMSMOTE method to address the imbalance properties and the MICE imputation method to impute the missing values in order to enhance the performance of the classifiers. Addition-ally, the boruta approach and recursive feature reduction has been utilized to identify the most important features while the hyper parameter tuning strategy has been employed to improve classifier performance and provide ideal solutions.Boruta opted just 50% of the total characteristics and outperformed RFE while considering the most important feature. Furthermore, many performance measures are used to determine which classifiers are the best in identifying OC. Voting classifier surpassed state-of-the-art approaches and other machine learning methods with the highest accuracy. The suggested approach obtained the highest average of 93.06% accuracy, 88.57% precision, 96.88% recall, 92.54% F1-score, and 93.44% AUC-ROC based on experimental results. Experiments show that in comparison with the state-of-the-art techniques, our suggested method can identify OC more accurately.

@article{P2025,
  title     = {Comparative Analysis of Machine Learning Frameworks for Robust Ovarian Cancer Detection Using Feature Selection and Data Balancing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {DSS LakshmiKumari P and Maragathavalli P},
  doi       = {10.14569/IJACSA.2025.0160687},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160687}
}