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 site 1 . 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.
Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain tumors in adults. Due to a low rate of survival in patients with these tumors, machine learning can help physicians for better decision-making. The aim of this paper is to develop a machine learning model for predicting the survival rate of patients with GBM based on clinical features and magnetic resonance imaging (MRI). Materials and Methods: The present investigation is an observational study conducted to predict the survival rate in patients with GBM in 12 months. Fifty-five patients who were registered in five Iranian Hospitals (Tehran) during 2012–2014 were selected in this study. Results: This study used Cox and C5.0 decision tree models based on clinical features and combined them with MRI. Accuracy, sensitivity, and specification parameters used to evaluate the models. The result of Cox and C5.0 for clinical feature was <32.73%, 22.5%, 45.83%>, <72.73%, 67.74%, 79.19%>, respectively; also, the result of Cox and C5.0 for both features was <60%, 48.58%, 75%>, <90.91%, 96.77%, 88.33%>, respectively. Conclusion: Using C5.0 decision tree model in both survival models including clinical features, both the imaging features and the clinical features as the covariates, shows additional predictive values and better results. The tumor width and Karnofsky performance status scores were determined as the most important parameters in the survival prediction of these types of patients.
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