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 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.

Machine Learning and Feature Selection Applied to SEER Data to Reliably Assess Thyroid Cancer Prognosis

“…ANNs are specifically designed to elucidate non-linear relationships, with an inherent ability to self-teach from training sets. Such algorithms are optimized to carry out image, facial, voice, and handwriting recognition, and now they have begun to be used in oncology research [ 4,5 , and this work].…”

The benefits of decision tree to predict survival in patients with glioblastoma multiforme with the use of clinical and imaging features

“…[ 4 ] Predicting the survival rate is difficult due to the existence of different factors such as the environment, genetics, and biology. [ 5 ] Physicians are still unable to accurately predict the relationship between health conditions, clinical findings, and survival. On the other hand, each treatment modality has some degree of negative side effects which lead some patients to die due to the complications of treatment.…”

“…One of the adequate methods to improve medical services and improve cancer treatment protocols is survival prediction which leads to assessing the efficacy of novel treatment strategies. [ 5 7 ] Designing a system which will bridge the gap between the medical system and survival prediction is important and will improve the quality of the treatment. [ 5 ] The most important purpose of the modeling process is to determine the relationships between variables and results which is an effective effort in improving the patients’ treatment.…”

“…[ 5 7 ] Designing a system which will bridge the gap between the medical system and survival prediction is important and will improve the quality of the treatment. [ 5 ] The most important purpose of the modeling process is to determine the relationships between variables and results which is an effective effort in improving the patients’ treatment. [ 8 ] Because of the complexity existing in health-care data, it is important to select a suitable modeling method based on existing data.…”

“…[ 8 9 ] Several statistical methods such as Cox and log logistic are used in majority of cancer researches. [ 5 ] Recently, machine learning methods develop and use in many of research. In fact, machine learning is a type of artificial intelligence through the aim of improving the machines to have the ability to extract knowledge and/or learn from the environment.…”