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Aim: The major goal of this research is to improve the accuracy of the Decision Tree (DT) and Support vector machine (SVM) algorithms and compare their efficiency in detecting breast cancer tumors. Materials and Methods: This work depends on the data obtained from the UCI Machine Learning Repository and used to acquire the data sets for the research of Innovative breast cancer prediction using machine learning algorithms. The sample size of breast cancer prediction involves two groups: Decision tree (N=20) and Support vector machine (N=20) according to clincalc.com by keeping 0.05 alpha error-threshold, 95% confidence interval, enrollment ratio as 0:1, and 80% G power. The accuracy, sensitivity, and precision are calculated using MATLAB software. Result: The accuracy of the DT is 83.83% (p |
