Breast Cancer Prediction using Machine Learning Algorithms

Abstract

The project is titled "Breast Cancer Prediction Using Machine Learning Algorithms." Breast cancer affects a significant number of women world-wide and ranks as the second most common cause of death among women. Early detection of breast cancer can drastically improve the prognosis and chances of survival by enabling timely clinical therapy. Furthermore, precise benign tumour classification can help patients avoid unnecessary treatment. The dataset for this study includes several clinical features such as insulin, glucose, resistin, adiponectin, homeostasis model assessment (HOMA), leptin, and monocyte chemoattractant protein-1 (MCP-1), along with age and body mass index (BMI). In this study, we will apply three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) to the Coimbra Breast Cancer Dataset (CBCD). After obtaining the results, a performance evaluation and comparison will be conducted among these different classifiers. This study aims to utilize machine learning algorithms for breast cancer prediction with a focus on identifying the most efficient classifiers through a comprehensive analysis of the confusion matrix, accuracy, and precision.