BREAST CANCER DIAGNOSIS BASED ON K-NEAREST NEIGHBORS: A REVIEW

Abstract

The techniques of machine learning are commonly used in classifying breast lesions, as they can improve the mammogram accuracy in detecting malignant masses. One of the top causes of death for women remains breast cancer. Early diagnosis can facilitate adequate treatment and reduce morbidity and mortality. Screening for cancer via mammogram can be an efficient method to detect breast lumps at an earlier stage. The main difficulty occurs during cancer detection and the distinction between a diagnosis to check whether a patient has a malignant or benign type of disease. Machine learning algorithms such as K-Nearest Neighbors classifier help solve this problem by providing high accuracy performance. K-Nearest Neighbors is one of the machine learning algorithms used to enhance the diagnostic accuracy of the mammogram. This paper reviews some recent studies that highlight K-Nearest Neighbors accuracy, as a machine learning algorithm, in diagnosing cancer of breast.