Intelligent personable treatment recommendation
Guidelines provide general information for cancer patients. However, different patients may perceive different degree of cancer lesion. The standard guidelines face limit to provide perfect treatment fit for every patient. In this project, data mining algorithms are proposed to provide personalized treatment recommendation by analysing the cancer datasets. The personalized treatment includes two aspects: personalized radiation therapy and personalized medicine. Our hypothesis is that data mining algorithm can train a model that can have all the available knowledge which is competent to an experienced expert. The national lung cancer screening trial (NLST) dataset provides an unparalleled resource about patients with diagnosis images, clinic data, treatment, nodule findings, demographics and other patient data. These data can be used to generate a knowledgeable data mining system so that the sophisticated personalized diagnostic decision-making process is possible.
A data mining system is developed by using the NLST dataset.