Using Data Mining and Association Rules for Early Diagnosis of Esophageal Cancer.

From 17,000 new cases of esophageal cancer worldwide during last year, 16,000 proved to be fatal. Late or incorrect diagnosis of esophageal cancer cases increases its fatality rate. Today, a data-mining technique can predict the course of the disease with the help of an upto-date technology. With this knowledge, we can reduce esophageal cancer mortality. This study aims to find an association between general characteristics, screening tests, and esophageal cancer based on raw data from the Cancer Research Center within-person interviews, using data mining and classification techniques on mortality. The 5-year medical records of 512 esophageal cancer patients and those with problems related to this cancer, with 50 functional characteristics, were included in this model. In order to provide a prognostic and rule discovery model for esophageal cancer suffering, we used preprocessing EM Algorithm. After accurate identification of the data, WEKA Software tools and Java programming language was used to create Association Rule Classifier and Apriori algorithm for the associated rule discovery. We created 6 significant rules of the association for classification generated by rule miner with 95% and 91% confidence based on screening tests and general attributes, respectively. These substantial rules showed significant association between age, history of medication, smoking, gender, carcinoembryonic antigen (CEA), creatinine, WBCs, and Platelets. The findings of this study can be used as a clue for physicians to consider patients with these characteristics as people who are more likely to develop esophageal cancer and help them for early diagnosis of patients. Keywords:Data mining, esophageal cancer, association rule, healthcare.

Effects of postural and cognitive difficulty levels on the standing of healthy young males on an unstable platform.

Standing on an unstable platform requires continuous effort of the neuro­musculoskeletal system. The aim of the present study is to evaluate the ability to remain standing on an unstable platform at different levels of postural and cognitive difficulty. Healthy young males stood in the sagittal plane on an unstable platform supported by a pair of springs with modifiable stiffness. The balance test also assessed different levels of vision and cognitive function. Linear and nonlinear metrics of standing, based on motion captured kinematic data, were assessed to analyze the stability of standing. Results showed that vision plays a significant role in maintaining balance in terms of linear metrics. Elimination of visual feedback changed the direction of body sway and increased standing instability. Placement of low stiffness springs led to unstable standing. The cognitive dual task, however, had no effect on the stability metrics and merely could be revealed in the simplest test condition. Standing on an unstable platform was closely related to visual feedback and decreasing the spring stiffness significantly reduced stability. The roles of cognitive involvement were subdued by increasing the postural difficulty in standing on an unstable platform.