Enhancing Prostate Cancer Classification by Leveraging Key Radiomics Features and Using the Fine-Tuned Linear SVM Algorithm
Künye
Metin Varan, Jahongir Azimjonov, & Bilgen MaÇal. (2023). Enhancing Prostate Cancer Classification by Leveraging Key Radiomics Features and Using the Fine-Tuned Linear SVM Algorithm. IEEE Access, 11, 88025–88039. https://doi.org/10.1109/access.2023.3306515 Özet
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. © 2013 IEEE.
WoS Q Kategorisi
Q2Kaynak
IEEE AccessCilt
11Koleksiyonlar
İlgili Öğeler
Başlık, yazar, küratör ve konuya göre gösterilen ilgili öğeler.
-
A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases
This study aims to use a machine learning (ML)-based enhanced diagnosis and survival model to predict heart disease and survival in heart failure by combining the cuckoo search (CS), flower pollination algorithm (FPA), ... -
The classification of wheat yellow rust disease based on a combination of textural and deep features
Hayıt, Tolga; Erbay, Hasan; Varçın, Fatih; Hayıt, Fatma; Akci, Nilüfer (Springer, 2023)Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and ... -
Utilizing Machine Learning Algorithms of Electrocardiogram Signals to Detect Sleep/Awake Stages of Patients with Obstructive Sleep Apnea
Uçar, Muhammed Kürşad; Bozkurt, Ferda; Bilgin, Cahit; Bozkurt, Mehmet Recep (Springer International Publishing Ag, 2020)Obstructive Sleep Apnea (OSA) is a respiratory-related disease that occurs during sleep. The diagnosis of OSA is made by a specialist doctor according to the records obtained with the polysomnography device. However, the ...