title: Prediction of survival in thyroid cancer using data mining technique.
abstract: Cancer is the second leading cause of death after cardiovascular diseases in the world. Health professionals are seeking ways for suitable treatment and quality of care in these groups of patients. Survival prediction is important for both physicians and patients in order to choose the best way of management. Artificial Neural Network (ANN) is one of the most efficient data mining methods. This technique is able to evaluate the relationship between different variables spontaneously without any prevalent data. In our study ANN and Logistic Regression were used to predict survival in thyroid cancer and compare these results. SEER (Surveillance, Epidemiology and End Result) data were got from SEER site1. Effective features in thyroid cancer have been selected based on supervision by radiation oncologists and evidence. After data pruning 7706 samples were studied with 16 attributes. Multi Layer Prediction (MLP) was used as the chosen neural network and survival was predicted for 1-, 3- and 5-years. Accuracy, sensitivity and specificity were parameters to evaluate the model. The results of MLP and Logistic Regression models for one year are defined as for 1-year (92.9%, 92.8, 93%), (81.2%, 88.9%, 72.5%), for 3-year as (85.1%, 87.8%, 82.8%), (88.6%, 90.2%, 87.2%) and for 5-year as (86.8%, 96%, 74.3%), (90.7%, 95.9%, 83.7) respectively. According to our results ANN could efficiently represent a suitable method of survival prediction in thyroid cancer patients and the results were comparable with statistical models.
authors.affiliation: Department of medical Informatics, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran. jajroudi@gmail.com.
authors.forename: M
authors.initials: M
authors.lastname: Jajroudi
authors.forename: T
authors.initials: T
authors.lastname: Baniasadi
authors.forename: L
authors.initials: L
authors.lastname: Kamkar
authors.forename: F
authors.initials: F
authors.lastname: Arbabi
authors.forename: M
authors.initials: M
authors.lastname: Sanei
authors.forename: M
authors.initials: M
authors.lastname: Ahmadzade
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve; D012307:Risk Factors; D018426:SEER Program; D013964:Thyroid Neoplasms / Q000401:mortality* / Q000473:pathology