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DOI: 10.14569/IJACSA.2023.0140843
PDF

Prostate Cancer Detection and Analysis using Advanced Machine Learning

Author 1: Mowafaq Salem Alzboon
Author 2: Mohammad Subhi Al-Batah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

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Abstract: Prostate cancer is one of the leading causes of cancer-related deaths among men. Early detection of prostate cancer is essential in improving the survival rate of patients. This study aimed to develop a machine-learning model for detecting and diagnosing prostate cancer using clinical and radiological data. The dataset consists of 200 patients with prostate cancer and 200 healthy controls and extracted features from their clinical and radiological data. Then, the data trained and evaluated using several machines learning models, including logistic Regression, decision tree, random forest, support vector machine, and neural network models, using 10-fold cross-validation. Our results show that the random forest model achieved the highest accuracy of 0.92, with a sensitivity of 0.95 and a specificity of 0.89. The decision tree model achieved a nearly similar accuracy of 0.91, while the logistic regression, support vector machine, and neural network models achieved lower accuracies of 0.86, 0.87, and 0.88, respectively. Our findings suggest that machine learning models can effectively detect and diagnose prostate cancer using clinical and radiological data. The random forest model may be the most suitable model for this task.

Keywords: Prostate cancer; machine learning; clinical data; radiological data; diagnosis; medical diagnosis

Mowafaq Salem Alzboon and Mohammad Subhi Al-Batah, “Prostate Cancer Detection and Analysis using Advanced Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140843

@article{Alzboon2023,
title = {Prostate Cancer Detection and Analysis using Advanced Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140843},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140843},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Mowafaq Salem Alzboon and Mohammad Subhi Al-Batah}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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