Machine Learning-Based Classification of Prostate Cancer Using Clinical Biomarkers
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Machine Learning-Based Classification of Prostate Cancer Using Clinical Biomarkers
Dr. N. Veerasekar
Assistant professor,
Department of
Biotechnology,
KIT-Kalaignarkarunanidhi Institute of
Technology,
Coimbatore, India.
Mohamed Asekh A
Student,
Department of
Biotechnology,
KIT-
Kalaignarkarunanidhi
Institute of
Technology,
Coimbatore, India.
Patrina JR
Student,
Department of
Biotechnology,
KIT -
Kalaignarkarunanidhi Institute of Technology,
Coimbatore, India.
Poorvanisha N
Student,
Department of
Biotechnology,
KIT-
Kalaignarkarunanidhi Institute of
Technology,
Coimbatore, India
ABSTRACT:
Prostate cancer is still one of the most common and fatal types of cancer in men worldwide. Early detection is the key to enhancing patient survival and reducing the use of invasive treatments. This research examines the use of machine learning as a tool for assisting the classification of prostate cancer cases based on standard clinical biomarkers. A public dataset of 100 patient samples was utilized, containing clinical features like PSA levels, perimeter, radius, texture, and more. A predictive model was created based on the XGBoost algorithm and its performance was tested using a 5-fold cross-validation method. The model resulted in a mean F1 score of 0.90 and a mean ROC–AUC of 0.93, indicating superior predictive capability. To improve interpretability, SHAP (Shapley Additive exPlanations) values were calculated, which identified PSA, perimeter, and texture as the most predictive features. These results indicate that machine learning algorithms, when combined with publicly available clinical biomarkers, can be used to develop trustworthy, interpretable, and cost-efficient tools for prostate cancer detection at an early stage. The method has the potential to be of use in supporting clinical decision-making and minimizing the necessity for invasive diagnostic testing.
KEYWORDS:
Prostate cancer, Machine learning, XGBoost algorithm, SHAP values, Clinical biomarkers
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