Machine Learning-Based Breast Cancer Detection
Sourabh Asharma, Siddhesh Shinde, Aditya Vikram Choudhary, Utkarsh Raj, Prashant Kumar Srivastava · 2024
Breast cancer is the most frequent and prevalent malignancy among Indian women. In general, if a patient's breast symptoms or the findings of an imaging test (such as a mammography) indicate that the patient might have breast cancer, the patient is advised to undergo a breast biopsy. This paper focusses on one of the most common breast biopsies which is Fine Needle Aspiration here. In Fine Needle Aspiration biopsy, a little amount of breast tissue or fluid is taken from a suspicious location with a thin, hollow needle and examined for cancer cells. The problem statement is to use Breast Cancer Wisconsin Dataset which contains the numerical values of features of each cell nucleus calculated after Fine Needle Aspiration biopsy, implement various machine learning classifiers on the dataset and estimate the type of breast cancer being, benign or malignant. The experimental results show that the accuracy of the predictions made by each classifier increases either by hyper- tuning the parameters of each classifier or optimizing the classifiers so that a higher accuracy of the predictions can be achieved, and maximum lives can be saved by the predictions made.