Prostate Cancer Detection
• Pre-process patient data (e.g., PSA levels, age, and biopsy results)
• Apply feature selection techniques to identify key predictors
• Build and train machine learning models (e.g., logistic regression, decision
trees, or SVM)
• Test model performance using cross-validation and validation datasets
• Compare the performance of machine learning models based on accuracy,
precision, and recall
Emmanuel Tjan