Prediction of survival in thyroid cancer using data mining technique.

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.

Epileptic EEG: a comprehensive study of nonlinear behavior.

In this study, the nonlinear properties of the electroencephalograph (EEG) signals are investigated by comparing two sets of EEG, one set for epileptic and another set for healthy brain activities. Adopting measures of nonlinear theory such as Lyapunov exponent, correlation dimension, Hurst exponent, fractal dimension, and Kolmogorov entropy, the chaotic behavior of these two sets is quantitatively computed. The statistics for the two groups of all measures demonstrate the differences between the normal healthy group and epileptic one. The statistical results along with phase-space diagram verify that brain under epileptic seizures possess limited trajectory in the state space than in healthy normal state, consequently behaves less chaotically compared to normal condition.