Classification Rules based Breast Cancer Detection using Machine Learning Approach

Abstract

One of the medical field's most researched issues is cancer diagnosis. Many researchers have concentrated on performance enhancement and achieving successful outcomes. One of the most lethal forms of cancer is breast cancer. A significant issue in cancer diagnosis research is the diagnosis of this cancer. A kind of artificial intelligence called machine learning allows a machine to grow over time. In bio informatics, machine learning is frequently employed, notably in the detection of breast cancer. supervised learning method known as K-nearest neighbors' approach, is one well-liked techniques. It's really intriguing to use the K-NN in medical diagnostics. The value of parameter "k" & distance have a significant impact on the findings' quality. This indicates how many neighbors are in proximity. In this paper, we assess the performance of various K-NN algorithmic distances. Additionally, we investigate this distance using various "k" parameter values and classification algorithms (the formula used to determine a sample's classification).