Breast Cancer Classification Model Using Decision Tree Algorithm - Nuzla Af'idatur Robbaniyyah; Ismi Asmawati; Syamsul Bahri (Jurnal Matematika, Statistika dan Komputasi, 2026)
Cancer is a disease characterized by the presence of abnormal cells or tissues that grow rapidly, uncontrollably, can spread to other parts of the patient's body and it can also sometimes be malignant. According to the International Agency for Research on Cancer, in 2024 breast cancer will rank second in terms of the highest number of cases and fourth as the leading cause of death globally. The objective of this study is to apply the Classification and Regression Tree (CART) decission tree algorithm to a breast cancer classification model based on patient medical records. The model developed has a specificity of 95.77%, recall, precision, and F1-Score of 93.02%, and accuracy of 94.74%. The model was evaluated using a confusion matrix to measure its performance. Thus, the CART algorithm can be applied in classification models, and the resulting model is considered optimal as it achieves percentages within the 90%-100% range for all performance evaluation metrics.