An Ontological Model based on Machine Learning for Predicting Breast Cancer

Breast cancer is mostly a female disease, but it may affect men as well even at a considerably lower percentage. An automated diagnosis system should be built for early detection because manual breast cancer diagnosis takes a long time. Doctors have lately achieved significant advances in the early identification and treatment of breast cancer in order to decrease the rate of mortality caused by the latter. Researchers, on the other hand, are analysing large amounts of complicated medical data by employing a combination of statistical and machine learning methodologies to assist clinicians in predicting breast cancer. Various machine learning approaches, including ontology-based Machine Learning methods, have lately played an essential role in medical science by building an automated system that can identify breast cancer. This study examines and evaluates the most popular machine learning algorithms, besides the ontological model based on Machine Learning. Among the classification methods investigated were Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, Artificial Neural Network, Random Forest, and k-Nearest Neighbours. The dataset utilized has 683 instances and is available for download from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Precision, and Recall. The ontology model surpassed all machine learning techniques, according to the results.

@article{Massari2022,
  title     = {An Ontological Model based on Machine Learning for Predicting Breast Cancer},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Hakim El Massari and Noreddine Gherabi and Sajida Mhammedi and Hamza Ghandi and Fatima Qanouni and Mohamed Bahaj},
  doi       = {10.14569/IJACSA.2022.0130715},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130715}
}