The application and analysis of the KNN algorithm in machine learning for breast cancer prediction

Abstract

When female patients are tested for cancer, breast cancer tends to be detected more often than other cancers. Breast cancer also accounts for a large percentage of deaths of female patients due to cancer. Timely prediction of whether one has breast cancer or not is of great significance in the treatment of breast cancer. In this study, K Nearest Neighbors (KNN) algorithm is used for data analysis as well as probability prediction to determine the probability of having breast cancer and compare it with traditional machine learning algorithms. The variation of accuracy versus K-value for a given dataset was experimentally represented by a graph. Also, the experiment gives and compares the accuracy of different learning algorithms in the same situation. According to the experimental results, the KNN algorithm tends to be more accurate than the other algorithms, with an accuracy of 98 percent. This turned out to be significantly higher than the remaining two algorithms. This experiment has significant implications for the healthcare industry. Because the use of this method can greatly improve the efficiency of doctors and the diagnosis of breast cancer, so that more people with the risk of breast cancer can be diagnosed and treated in a timely manner. In addition, hospitals can use the study to predict the incidence of breast cancer in a given area of the population.