Artificial Intelligence Model Selection for Breast Cancer Risk Screening

Abstract

Ziwen Fang

In today's social environment, the risk of breast cancer for women is increasing, and breast cancer has exceeded lung cancer as the most common cancer nowadays. However, if detect breast cancer at an early stage and measures are taken, it can be very effective in improving the chances of survival of breast cancer patients. Meanwhile, with the continuous development of artificial intelligence, it shows a broad prospect in the medical field. In this article experiment try to apply AI to the field of breast cancer risk detection, and help improve the accuracy of breast cancer screening by finding the artificial intelligence model with the highest accuracy rate. This article selected breast cancer data from kaggle, pre-processed the data by Pearson Correlation Coefficient, and then the article compares four of the most common machine learning algorithms namely Random Forest, Logistic Regression, Neural Networks, and Support Vector Machines, using Python. Based on the experimental results the article conclude that Random Forest is highly accurate and shows great affect in the field of breast cancer screening.

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