Machine Learning-Based Prediction of Elevated Prostate-Specific Antigen Levels from Lifestyle and Demographic Data

Background: Machine learning offers promising approaches for medical prediction tasks. This study evaluates the comparative performance of three ML algorithms: Logistic Regression with L1 regularization, Support Vector Machine (SVM), and Random Forest in predicting elevated prostate-specific antigen (PSA) levels using lifestyle and demographic features. Objectives: To compare the predictive performance, generalization capability, and stability of multiple ML models for detecting elevated PSA levels via binary classification of PSA status. Methods: We implemented three ML algorithms with two feature selection approaches to address the events-per-variable (EPV) problem. Lifestyle and demographic data were collected from adult males in Etsako West LGA, Edo State, Nigeria. Models were trained on 70% of the data (n=69) and validated on 30% (n=30). Performance was assessed using accuracy, precision, recall, specificity, F1-score, and ROC-AUC. Five-fold stratified cross-validation evaluated model stability. Hyperparameter optimization was performed using GridSearchCV. Results: The Random Forest model achieved the most balanced performance with 73.3% accuracy, 70.6% precision, 80.0% recall, 63.6% specificity, 0.750 F1-score, and 0.764 ROC-AUC. SVM showed identical test set performance (73.3% accuracy, 0.764 ROC-AUC). Logistic Regression with L1 regularization and 11 features achieved the highest recall (100%) but at the cost of zero specificity, indicating overfitting. Cross-validation revealed model stability: Random Forest CV recall 0.833 ± 0.061, CV F1 0.813 ± 0.053. The F1-optimized Random Forest showed improved balance (70.0% accuracy, 66.7% recall, 50.0% specificity). All models demonstrated ROC-AUC between 0.714–0.764, indicating acceptable discrimination capability. Conclusions: Random Forest and SVM demonstrated the most balanced performance in terms of sensitivity and specificity for PSA prediction in a small-sample setting. The study highlights important methodological considerations, including the need for feature selection under EPV constraints, the role of regularization in mitigating overfitting, and the importance of cross-validation for evaluating models out of sample performance.The moderate performance (ROC-AUC ≈ 0.71–0.76) suggests that lifestyle-based ML models may be useful for preliminary screening but are not suitable for diagnostic applications. Future work should focus on larger datasets, external validation, and ensemble methods.