Evaluating Machine Learning Models for Predicting Prostate Cancer Progression Using Lifestyle Factors: A Systematic Review and Meta-Analysis
DOI:
https://doi.org/10.70112/ajeat-2024.13.1.4241Keywords:
AUC (Area Under the Curve), Lifestyle Factors, Logistic Regression, Machine Learning, Multi-Layer Perceptron, Precision Oncology, Prostate Cancer, Support Vector MachineAbstract
This systematic review and meta-analysis evaluated the performance of machine learning models in predicting prostate cancer progression using lifestyle factors as predictive biomarkers to improve prognostic accuracy. Various models were analyzed, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN). These models were applied to identify diagnostic and prognostic biomarkers and to enhance the forecasting of prostate cancer progression. The meta-analysis demonstrated high predictive effectiveness across models, with mean performance metrics of 0.901 AUC (Area Under the Curve), 0.914 F1 Score, 0.889 accuracy, and 0.914 sensitivity. Among the models, the Multi-Layer Perceptron (MLP) emerged as the most effective, achieving 97% accuracy and an AUC of 95.8%. These findings underscore the potential of machine learning to integrate lifestyle factors as predictive biomarkers, advancing precision oncology in prostate cancer care.
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