Breast Cancer Prediction Using Deep Learning Models
Rakesh Kumar, Meenu Gupta, Anmol Yadav, Mukul Gautam · 2023
Breast cancer is a serious health issue affecting millions of women worldwide, and early detection is essential for successful treatment. In this study, we propose a breast cancer prediction model that utilizes machine learning algorithms to accurately predict the likelihood of breast cancer in patients. Our dataset includes clinical and demographic information of patients, such as age, family history, and mammography results. We preprocess the data and use feature engineering techniques to extract relevant information. We apply various machine learning algorithms, such as logistic regression, decision trees, random forest, and support vector machines, to develop the breast cancer prediction model. We evaluate the performance of the model using metrics such as accuracy, sensitivity, and specificity. Our results show that the proposed breast cancer prediction model achieved high accuracy and sensitivity in identifying patients at high risk of breast cancer. The model can be used by healthcare professionals to identify patients who require further screening and follow-up. Our breast cancer prediction model offers a valuable tool for early detection and diagnosis of breast cancer, potentially leading to improved outcomes and survival rates.