Predicting overall survivability in comorbidity of cancers: A data mining approach

Cancer and other chronic diseases have constituted (and will do so at an increasing pace) a significant portion of healthcare costs in the United States in recent years. Although prior research has shown that diagnostic and treatment recommendations might be altered based on the severity of comorbidities, chronic diseases are still being investigated in isolation from one another in most cases. To illustrate the significance of concurrent chronic diseases in the course of treatment, this study uses SEER's cancer data to create two comorbid data sets: one for breast and female genital cancers and another for prostate and urinal cancers. Several popular machine learning techniques are then applied to the resultant data sets to build predictive models. Comparison of the results shows that having more information about comorbid conditions of patients can improve models' predictive power, which in turn, can help practitioners make better diagnostic and treatment decisions. Therefore, proper identification, recording, and use of patients' comorbidity status can potentially lower treatment costs and ease the healthcare related economic challenges. •To enable prospective analyses, data about patients’ main diseases and comorbid conditions should be stored together.•More information about comorbid diseases can improve models' predictive power•A predictive model that does not filter the cases based on their final outcome has a greater practical significance•More accurate predictive models for chronic diseases can potentially lower treatment costs and economic losses