Optimization of Breast Cancer Prediction using Optimaze Parameter on Machine Learning - Nuarini, Sri; Rumintarsih, Ade (Journal Medical Informatics Technology, 2023)
At present, a very common cancer disease in women is breast cancer. This cancer develops in the female breast tissue and is the cancer with the highest mortality rate. This needs great attention. Forecasting breast cancer has been studied by a number of researchers and is considered a serious threat to women. Clinical difficulties in creating treatment approaches that will help patients live longer, due to the lack of solid predictive models that can predict outcomes at an early stage by analyzing patient history data. Because it can affect women all over the world. Early detection of breast cancer is crucial in determining the path of action. Cancer types can be distinguished into two types: benign and malignant. this research aims to provide information and science to medical professionals and also cancer patients to know the classification of the two types of cancer. The research project aims to also leverage data mining techniques using several algorithms on Machine Learning (ML) such as Decision Tree(DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting Tress (XGBoost). The results of this algorithm will determine the prediction of the most common types of cancer. The study used 683 samples of breast cancer patients, including 10 characteristics. This test is measured through mammography and biopsy tests. Using K-Fold Validation operators, then the sresults of the study showed that the K-Nearest Neighbor (KNN) algorithm produced the highest accuracy of 96.87% compared to the other five algorithms. Then, as a comparison again, the researchers also optimized the accuracy value using the parameter optimize operator. Where the number produced becomes more overwhelming. The highest accuracy result after calculated with the parameter optimize is the Random Forest (RF) algorithm. Where the result is 100% accurate compared to other ML algorithms.