Can Machine Learning Technique Predict the Prostate Cancer accurately?: The fact and remedy

As a potentially deadly human malignancy, prostate cancer is unique in that it has a high frequency of histologic changes associated with cancer but a low prevalence of the clinical disease. Despite improved early detection and treatment techniques, the diagnosis of malignancies is frequently not established until the illness has progressed locally or metastatically. Most tumors identified are latent, presenting no threat to the host's life or health. Doctors are wary of using these tests for fear of needless morbidity. Cancers latent or “clinically inconsequential” have a larger volume, a higher grade, and are more invasive. Current diagnostics can only identify tumors large enough to feel, see on ultrasound, or raise blood levels of prostate-specific antigen. If the patient's life expectancy is sufficient and the treatment's morbidity is low, these malignancies should be treated curatively. This study addresses these concerns by predicting prostate cancer using six (6) machine learning techniques: Random Forest, SVM, KNN, Logistic Regression, Neural Network, and the Ensemble model. We gathered data for 100 patients who were placed in ten different circumstances. The data was categorized as malignant or non-cancerous. Among the six machine learning techniques, logistic regression, neural networks, and ensemble learning have the potential to reach an accuracy of 95.00 percent. Ensemble learning can detect 96.55% of true positive prostate cancer in our model. KNN has a 90% accuracy rate, whereas SVM and Random Forest have an 85% accuracy rate.