Version 1
: Received: 1 July 2023 / Approved: 3 July 2023 / Online: 3 July 2023 (11:30:41 CEST)
How to cite:
SANTOS, D. Performance Evaluation of Machine Learning Models for Prostate Cancer Detection. Preprints2023, 2023070067. https://doi.org/10.20944/preprints202307.0067.v1
SANTOS, D. Performance Evaluation of Machine Learning Models for Prostate Cancer Detection. Preprints 2023, 2023070067. https://doi.org/10.20944/preprints202307.0067.v1
SANTOS, D. Performance Evaluation of Machine Learning Models for Prostate Cancer Detection. Preprints2023, 2023070067. https://doi.org/10.20944/preprints202307.0067.v1
APA Style
SANTOS, D. (2023). Performance Evaluation of Machine Learning Models for Prostate Cancer Detection. Preprints. https://doi.org/10.20944/preprints202307.0067.v1
Chicago/Turabian Style
SANTOS, D. 2023 "Performance Evaluation of Machine Learning Models for Prostate Cancer Detection" Preprints. https://doi.org/10.20944/preprints202307.0067.v1
Abstract
This article presents a comparative analysis of three Machine Learning models, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, for prostate cancer detection. The models were trained and evaluated using clinical data, and their performance was assessed using various evaluation metrics. The results show that Logistic Regression achieved the highest accuracy (90%) among the three models, followed by Random Forest Classifier (76.67%) and Decision Tree Classifier (73.33%). Similarly, Logistic Regression demonstrated superior precision (95.65%) and F1 Score (93.62%), indicating its effectiveness in identifying true positive cases. However, the Decision Tree Classifier exhibited higher recall for the negative class (83.33%) compared to the positive class (70.83%), while Random Forest Classifier showed balanced recall for both classes (66.67% for negative and 79.17% for positive). These findings suggest that Logistic Regression outperforms the other models in terms of accuracy and precision, while the Decision Tree Classifier and Random Forest Classifier provide better recall for certain classes. The results highlight the potential of Machine Learning in prostate cancer detection and provide insights for further research and improvement of the models.
Keywords
prostate cancer detection; machine learning; logistic regression; decision tree classifier; random forest classifier;
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
