Enhancing Prostate Cancer Classification by Leveraging Key Radiomics Features and Using the Fine-Tuned Linear SVM Algorithm

Abstract

This paper focuses on enhancing machine learning (ML)-based diagnosis and clinical decision-making by leveraging radiomics data, which provides a quantitative description of grayscale medical images such as MRI, CT, PET, or X-Ray. Extracted using advanced mathematical and statistical analysis methods, this data comprises hundreds of relevant and irrelevant radiomics features. The study underscores the critical importance of selecting the most relevant and efficient features to enhance ML-based diagnosis and clinical decision-making processes. To address this challenge, the paper introduces an accurate binary prostate cancer classification algorithm that integrates linear support vector machines (SVM) and ridge regression-based four-feature selection algorithms. The algorithm’s performance was evaluated using the PROSTATEx dataset. Notably, when trained on feature subsets selected through importance coefficient, forward- and backward-sequential, and correlation coefficient-based feature selectors, the algorithm achieved classification accuracy exceeding 90%. However, when trained on the full set of features, the algorithm achieved 43.64% classification accuracy. These findings underscore the pivotal role of feature selection in achieving higher accuracy and speed during the training and testing of ML algorithms. Overall, the results indicate that the proposed algorithm can substantially improve the accuracy of prostate cancer classification. Furthermore, the findings have broader implications for the development of more efficient ML-based diagnosis and clinical decision-making systems in the field of gray-scale medical imaging analysis.

Keywords

• Radiomics feature extraction
• efficient radiomics features
• random search-based hyper parameter tuning
• fine-tuned linear SVM classifier
• fine-tuned ridge regressor
• feature selection algorithms