Brain cancer is one of the most deadly cancers, with a very low survival rate. By understanding the factors that lead to cancer spreading, practitioners can concentrate their efforts on providing the most effective treatment, and they can modify the treatment plan as necessary. Also, knowing the likelihood of a patient's survival over a specified time period can enable them to make informed decisions about adjusting their routines, future investments, and other health-related decisions. The use of data-driven models in cancer research has gained increased popularity over the past several decades. Moreover, there is still much uncertainty surrounding the factors that contribute to survival of cancer, making it difficult to develop a model. The existing literature on brain cancer contains a variety of machine learning models. However, many of them lack a high degree of accuracy, and, in medical research, accuracy is essential to the proper guidance of treatment decisions. Therefore, we have proposed a framework comprising multiple phases of classical statistics and machine learning methods to find a parsimonious model with a high degree of accuracy (98.9%) for predicting brain cancer survivability. Furthermore, we develop a prototype web-based interactive tool to facilitate the practical implementation of the proposed model and provide a deeper understanding of how a particular factor affects survival when other factors remain unchanged. By integrating this tool into healthcare settings, medical professionals can rapidly detect potentially vulnerable patients, and it can also be useful in determining the most effective treatment plan.
All Science Journal Classification (ASJC) codes
- Health Informatics
- Analytical Chemistry