A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer

Prostate cancer is one of the leading causes of cancer-related deaths among men. Early detection of Prostate cancer is important in improving the survival rate of patients. In this study, we aimed to develop a machine learning model for the detection and diagnosis of Prostate cancer using clinical and radiological data. We used a dataset of 200 patients with Prostate cancer and 200 healthy controls and extracted a set of features from their clinical and radiological data. We then trained and evaluated 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 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 be effective in detecting and diagnosing Prostate cancer using clinical and radiological data, and that the random forest model may be the most suitable model for this task.