Performance Evaluation Metrics for Machine Learning Classification Techniques In a Oncological Disease Prediction

INDIA, Reported nearly 13, 24, 413 new cancer incidences in 2020. Prostate cancer, an oncological disease cases are also significantly increased with an increasing mortality rate. Accurate prediction tools are important in prostate cancer detection. Machine learning principles are best suited for developing modern applications in healthcare informatics and biomedical Engineering and sciences. The proposed paper discussed ML based classifier techniques for prediction of affected prostate cancer glands. Logistic regression decision tree, k nearest neighbor and naïve Bayes classifier are implemented for the prostate cancer patient data. Comparative analysis with the help of classification metrics is presented. The prediction accuracy using logistic regression is calculated as 91% which is better than the 73% for K nearest Neighbor classifier techniques is 73%.