Integrating Oncology Prediction and Prognostication for Precision Medicine Decision-Making

Abstract

Breast cancer continues to be a primary cause of cancer-related mortality in women worldwide, highlighting the need for the creation of effective predictive models to facilitate early identification and treatment. This project is about building a machine learning-based prediction model using linear regression to estimate the likelihood that breast cancer is malignant or benign based on different clinical criteria. The project is divided into three parts: Data Collection, Model Training, and Prediction. It uses the breast cancer dataset from the "sklearn" package. The Data Collection Module loads and preprocesses the dataset to make sure it is clean and ready for analysis. The Model Training Module uses linear regression to train the model and assesses its effectiveness using accuracy metrics on both the training and test datasets. The Prediction Module makes predictions in real time, so users can add new data and receive instant feedback on potential diagnoses. The study shows how well machine learning works in medicine by putting these parts together. It also shows how important it is to make accurate, on-time predictions to improve patient outcomes. Future improvements might include exploring more complex algorithms, improving model performance, and adding more datasets. All of these things would help with continued research and detection of breast cancer. By bringing these parts together, the project shows how useful machine learning can be in medicine and how important it is to make accurate, timely predictions to improve patient outcomes. Future improvements may include exploring more complex methods, tuning hyperparameters to improve performance, and adding more datasets to strengthen the model. All of these things would help with breast cancer research and detection.