Comparison of supervised machine learning methods for prostate cancer predication.

After lung cancer, prostate cancer is the most common form of cancer among men. World Cancer Research Fund International estimates that 1,414,259 people will be diagnosed with cancer in 2020, with 375,304 people losing their lives to the disease [1]Predicting the onset of Prostate Cancer at an early stage is, thus, essential. One of the major problems in Pca is diagnosing the disease as it does not show any early signs or symptoms. There are various diagnosing techniques for Pca. These techniques are very painful for the patients; one is DRE (Digital Rectal Examination). This is an ideal situation for using machine learning techniques to find an answer. Various supervised learning techniques (such as Random Forest, K-nearest Neighbor, SVM (Support Vector Machine), Logistic Regression, MLP (Multi-layer Perceptron), RNN (Recurrent Neural Network), and Naive Bayes) will be compared and contrasted in this study to make accurate predictions about prostate cancer. The primary goal of the research is to identify the most effective strategy for the prediction of prostate cancer for future use. Data for the study was used from the open-access online site of 100 patients. These Methods were evaluated basedon their precession, F1-Score, accuracy, recall and AUC. The results show that MLP, Logistic Regression, RNN performed well with 93% accuracy. Therefore, we may conclude that this method has huge benefits for the detection of prostate cancer. [ABSTRACT FROM AUTHOR]