Karnauph classifier for predicting breast cancer based on morphological features

Breast cancer is the primary cause of mortality in women in the world, using artificial intelligence in predicting, detecting and early diagnosing of breast cancer can reduce the mortality rate. In this study, we propose a new approach to find the best morphological features that give the best prediction accuracy about the type of cancer benign or malignant. Our proposed algorithm Karnauph algorithm is a hybrid mathematical model that combines between features from the supervised learning and some other features from the unsupervised learning to obtain an easy and robust algorithm in term of prediction and computation. We apply our proposed algorithm on breast cancer dataset and we compare its performance in term of prediction accuracy with that of Naive Bayes, decision tree and Adaboost algorithms. The results show that our algorithm performance is similar to all of these algorithms when they are implemented on the same dataset and it gives better performance than other algorithms when evaluated on unseen data. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.