The comparison of machine learning methods for the detection of breast cancer - Derisma, Derisma; Silvana, Meza (Jurnal Informatika, 2020)
Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python programming and public dataset i.e. MIAS dataset. This dataset has been proven and widely used for a modeling and application of breast cancer classification. Feature extraction used Gray Level Co-occurrence Matrix (GLCM). The machine learning methods that were applied in this study were Decision Tree, SVM, Random Forest, Multilayer Perceptron, KNN, Logistic Regression and Naïve Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 100% accuracy rate and Naïve Beyes was the lowest with 63% of accuracy rate.