| E. Dritsas, M. Trigka, Ph. Mylonas |
| Machine Learning Models for Prostate Cancer Identification |
| 15th International Joint Conference on Knowledge Discovery and Information Retrieval (KDIR 2023), 13-15 November 2023, Rome, Italy |
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ABSTRACT
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| In the present research paper, we focused on prostate cancer identification with machine learning (ML) techniques and models. Specifically, we approached the specific disease as a 2-class classification problem by categorizing patients based on tumour type as benign or malignant. We applied the synthetic minority oversampling technique (SMOTE) in our ML models in order to reveal the model with the best predictive ability for our purpose. After the experimental evaluation, the Rotation Forest (RotF) model overcome the other ones, achieving an accuracy, precision, recall, f1-score of 86.3%, and an AUC equal to 92.4% after SMOTE with 10-fold cross-validation.
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| 13 November , 2023 |
| E. Dritsas, M. Trigka, Ph. Mylonas, "Machine Learning Models for Prostate Cancer Identification", 15th International Joint Conference on Knowledge Discovery and Information Retrieval (KDIR 2023), 13-15 November 2023, Rome, Italy |
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