Welcome to Cancer Treatment Predictor
This application helps predict the best treatment for cancer patients based on their characteristics.
Start PredictionAbout This Project
The Cancer Treatment Predictor is an innovative application designed as part of a research study at Boston Medical Center (BMC) under the direction of Dr. Anand K. Devaiah, MD. The primary objective of this project is to leverage machine learning techniques to predict survival outcomes and recommend optimal treatment strategies for cancer patients. This tool integrates various patient characteristics, including age, sex, race, tumor grade, and treatment history, to provide personalized treatment recommendations.
The model is built using a deep learning neural network trained on extensive patient data. By analyzing historical survival data, the application helps identify the most effective treatment options for individual patients, potentially improving survival rates and quality of life.
Research Significance
This project has the potential to transform how treatment decisions are made in oncology. By providing clinicians with data-driven insights, the tool can support personalized medicine, helping to tailor treatments to the unique needs of each patient. This approach aligns with the broader goals of precision medicine, which seeks to improve patient outcomes by considering individual variability in genes, environment, and lifestyle.
Potential Limitations
While this application is designed to assist in treatment decision-making, it is important to note that the predictions are based on historical data and machine learning models. These models may not fully capture the complexities of individual cases or the latest advancements in medical research. Therefore, the recommendations provided by this tool should be used in conjunction with clinical judgment and not as a sole basis for treatment decisions.