Case data-mining analysis for patients with oesophageal cancer

We are in an era of digital medicine in which physicians can generate copious patient data, but tools to analyse these data are limited. Thus, we used case data from patients with oesophageal cancer from a medical institution, removed incomplete information, and quantified the textual data according to recommendations from the corresponding physicians. We used different classification algorithms to process the data, predict patient survival, and compare accuracies across algorithms. Our experimental results show that the BayesNet algorithm was highly accurate and precise, and, thus, may represent a promising data-mining tool.