Title :
Predicting patients survival using supervised techniques
Author :
Mutalib, Sofianita ; Azman, Nor Aina ; Abdul-Rahman, Shuzlina
Author_Institution :
Fac. of Comput. & Math. Sci., UiTM Shah Alam, Shah Alam, Malaysia
Abstract :
This paper attempts to predict the survival of patients using supervised machine learning techniques. To predict this task, the variables were identified and retrieved from the StatLib database. Both the artificial neural networks and linear regression models were used to perform the task. Experimental results, based on the classification accuracy were analysed from training and testing datasets. To increase the performance generalisation, data were randomly divided into three different datasets and experimented. Results showed that the artificial neural networks model outperformed the linear regression models in most cases.
Keywords :
health care; learning (artificial intelligence); neural nets; regression analysis; StatLib database; artificial neural networks; linear regression model; patients survival prediction; supervised machine learning; supervised techniques; Accuracy; Analytical models; Artificial neural networks; Linear regression; Machine learning; Physiology; Predictive models; Artificial Neural Networks; Linear Regression; Patients Survival; Prediction;
Conference_Titel :
Hybrid Intelligent Systems (HIS), 2011 11th International Conference on
Conference_Location :
Melacca
Print_ISBN :
978-1-4577-2151-9
DOI :
10.1109/HIS.2011.6122176