Hyperparameter Tuning Algoritma KNN Untuk Klasifikasi Kanker Payudara Dengan Grid Search CV

Abstract

One of the deadliest diseases in the world is Breast cancer. Breast cancer is a disease caused by abnormal cells that grow and develop rapidly and malignantly in the human breast and spread quickly to the tissues or organs around the breast. Data from Riskesdas in 2019 stated that in Indonesia, the prevalence of breast cancer was 41.2 per 100,000 Indonesians with an average death rate of 17 per 100,000 Indonesians. Technology nowadays is increasingly advanced and developed which can help people to find out the disease they are suffering from early before carrying out further examinations with the doctor. Breast cancer can be detected early by classifying it with machine learning algorithm. In this research, Breast cancer will be classified using K-Nearest Neighbor algorithm with Grid Search to classify whether a person has breast cancer or not. K-Nearest Neighbor (KNN) is one of the classification algorithms, where classification is carried out on data objects based on learning data whose neighbors are closest to the data object. The performance results of the classification model using K-Nearest Neighbor are 83% accuracy, 73% precision, and 89% recall.