Analyzing a Lung Cancer Patient Dataset with the Focus on Predicting Survival Rate One Year after Thoracic Surgery

Abstract

Background: Data mining, a new concept introduced in the mid-1990s, can help researchers to gain new, profound insights and facilitate access to unanticipated knowledge sources in biomedical datasets. Many issues in the medical field are concerned with the diagnosis of diseases based on tests conducted on individuals at risk. Early diagnosis and treatment can provide a better outcome regarding the survival of lung cancer patients. Researchers can use data mining techniques to create effective diagnostic models. The aim of this study was to evaluate patterns existing in risk factor data of for mortality one year after thoracic surgery for lung cancer. Methods: The dataset used in this study contained 470 records and 17 features. First, the most important variables involved in the incidence of lung cancer were extracted using knowledge discovery and datamining algorithms such as naive Bayes, maximum expectation and then, using a regression analysis algorithm, a questionnaire was developed to predict the risk of death one year after lung surgery. Outliers in the data were excluded and reported using the clustering algorithm. Finally, a calculator was designed to estimate the risk for one-year post-operative mortality based on a scorecard algorithm. Results: The results revealed the most important factor involved in increased mortality to be large tumor size. Roles for type II diabetes and preoperative dyspnea in lower survival were also identified. The greatest commonality in classification of patients was Forced expiratory volume in first second (FEV1), based on levels of which patients could be classified into different categories. Conclusion: Development of a questionnaire based on calculations to diagnose disease can be used to identify and fill knowledge gaps in clinical practice guidelines.