Comparing the Performance of Various Supervised Machine Learning Techniques for Early Detection of Breast Cancer

Cancer is a fatal disease that constantly changing and affects vast number of individuals worldwide. At the research level, much work has gone into creation improvement techniques built on data mining approaches allow for early identification prevention breast cancer. Because its excellent diagnostic abilities effective classification, technologies have reputation in medical profession continually increasing. Data machine learning can aid practitioners conceiving developing tools to detection As result, goal this compare different algorithms order determine best way detecting cancer promptly. This study assessed classification accuracy four algorithms: KNN, Decision Tree, Naive Bayes, SVM find accurate supervised algorithm might be used diagnose Bayes maximum supplied dataset, according prediction results. reveals that, when compared SVM, utilized predict