A Comparison of Supervised Learning Techniques for Predicting the Mortality of Patients with Altered State of Consciousness

Muhammad Ariff Yasri, Shamimi A. Halim, Muthukkaruppan Annamalai

Abstract


The study attempts to identify a potentially reliable supervised learning technique for predicting the outcomes of mortality in an altered state of consciousness (ASC) patients. ASC is a state distinguished from ordinary waking consciousness, which is a common phenomenon in the Emergency Department (ED). Thirty (30) distinctive attributes or features are commonly used to recognize ASC. The study accordingly applied these features to model the prediction of mortality in ASC patients. Supervised learning techniques are found to be suitable for such classification problems. Consequently, the study compared five supervised learning techniques that are commonly applied to evaluate the risk of mortality using health-related datasets, namely Decision Tree, Neural Network, Random Forest, Naïve Bayes, and Logistic Regression. The labeled dataset comprised patient records captured by the Universiti Sains Malaysia hospital’s Emergency Medicine department from June to November 2008. The cleaned dataset was divided into two parts. The larger part was used for training and the smaller part, for evaluation. Since the ratio between training and testing samples varies between individual supervised learning techniques, we studied the performance of the modeled techniques by also varying the proportion of the training data to the dataset. We applied four percentage splits; 66%, 75%, 80%, and 90% to allow for 3-, 4-, 5- and 10-fold cross-validation experiments to evaluate the accuracy of the analyzed techniques. The variation helped to lessen the chance of over fitting, and averaged the effects of various conditions on accuracy. The experiments were conducted in the WEKA environment. The results indicated that Random Forest is the most reliable technique to model for predicting the mortality in ASC patients with acceptable accuracy, sensitivity, and specificity of 70.9%, 76.3%, and 65.5%, respectively. The results are further confirmed by SROC analysis. The findings of the study serve as a fundamental step towards a comprehensive study in the future.

Keywords


supervised learning technique; predictive modelling; mortality; altered state of consciousness.

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References


R. C. Deo, “Machine Learning in Medicine”, Circulation, vol. 132, pp. 1920–1930, 2015.

A. Ławrynowicz and V. Tresp, Introducing Machine Learning. Perspectives on Ontology Learning, AKA Heidelberg, 2014.

R. Ahmad and M. Masilamany, Non-Traumatic Altered States of Consciousness, Lambert Academic Publishing, Berlin, 2010.

W. Kanich, W. J. Brady, J. S. Huff, A. D. Perron, C. Holstege, G. Lindbeck and C. T. Carter, “Altered Mental Status: Evaluation and Etiology in the ED,” American Journal of Emergency Medicine, vol. 20, no. 7, pp. 613–617, 2002.

D. Vaitl, N. Birbaumer, J. Gruzelier, G. Jamieson, B. Kotchoubey, A. Kubler and T. Weiss, “Psychobiology of Altered States of Consciousness,” Psychology of Consciousness: Theory, Research, and Practice, vol. 1, pp. 2-47, 2013.

P. Lai, G. Yiang, M. Tsai and S. Hu, “Analysis of Patients with Altered Mental Status in an Emergency Department of Eastern Taiwan,” Tzu Chi Medical Journal, vol. 21, no. 2, pp. 151–155, 2009.

J. L. Song, V. J. Wang and M. N. Vasquez, “Altered Level of Consciousness: Evidence-based Management in the Emergency Department,” Pediatr Emerg Med Pract, vol. 14, no. 1, 2017.

M. Almardini and Z. W. Raś, A Supervised Model for Predicting the Risk of Mortality and Hospital Readmissions for Newly Admitted Patients, LNCS, 2017, vol. 10352.

F. Prior, “Medical Knowledge Discovery and Management,” Journal of Military Medicine, vol. 5, no. 21, pp. 21–26, 2009.

D. Simona and M. Michael, “Churn analysis - Machine learning,” Indiana University, Project Report, 2017.

C. E. Sapp, “Preparing and architecting for machine learning,” Gartner Technical Professional Advice, 2017.

A. J. Tallón-Ballesteros and J. C. Riquelme, Data Cleansing Meets Feature Selection: A Supervised Machine Learning Approach, J. Ferrandez Vicente, J. Alvarez-Sanchez, F. de la Paz Lopez, F. Toledo-Moreo and H. Adeli Eds., LNCS, Cham: Springer, 2015, vol. 9108.

E. Acuña and C Rodriguez, The Treatment of Missing Values and its Effect on Classifier Accuracy, D. Banks, F.R. McMorris, P. Arabie and W. Gaul, Eds., Classification, Clustering, and Data Mining Applications Studies in Classification, Data Analysis and Knowledge Organisation. Berlin: Springer, 2004.

K. K. Dobbin and R. M. Simon, “Optimally Splitting Cases for Training and Testing High Dimensional Classifiers,” BMC Medical Genomics, vol. 4, no. 1, 2011.

M. A. Shahin, H. R. Maier and M. B. Jaksa, “Data Division for Developing Neural Networks Applied to Geotechnical Engineering,” Journal of Computing in Civil Engineering, vol. 18, no. 2, pp. 105–114, 2004.

B. W. Wanjawa, “Predicting future Shanghai stock market price using ANN in the period 21-Sep-2016 to 11-Oct-2016,” Preprint arXiv:1609.05394, 2016.

S. Borra and A. Di Ciaccio, “Measuring the Prediction Error: A Comparison of Cross-Validation, Bootstrap and Covariance Penalty Methods,” Computational Statistics & Data Analysis, vol. 54, no.12, pp. 2976–2989, 2010.

N. V. Chawla, Data Mining for Imbalanced Datasets: An Overview, O. Maimon and L. Rokach, Eds., Data Mining and Knowledge Discovery Handbook. Boston: Springer, 2005.

S. B. Aher and L. M. R. J Lobo, “Data mining in educational system using weka,” in Proc. ICEET’11, pp. 20–25.

E. Gladstone, K. Smolina, S. G. Morgan, K. A. Fernandes, D. Martins and T. Gomes, “Sensitivity and Specificity of Administrative Mortality Data for Identifying Prescription Opioid-related Deaths,” Canadian Medical Association Journal, vol. 188, no. 4, pp. 67–72, 2016.

E. Zriqat, A. Altamimi and M. Azzeh, “A Comparative Study for Predicting Heart Diseases using Data Mining Classification Methods,” International Journal of Computer Science and Information Security, vol. 14, no. 12, pp. 868–879, 2016.

J. Varun and G. Sunila, “Comparative Study of Data Mining Classification Methods in Brain Tumor Disease Detection,” International Journal of Computer Science & Communication, vol. 8, no. 2, pp. 12–17, 2017.

S. Venkataraman, “System design for large scale machine learning,” University of California, Berkeley, Tech. Rep. UCB/EECS-2017-219, 2017.

S. Sakr, R. Elshawi, A. M. Ahmed, W. T. Qureshi, C. A. Brawner, S. J. Keteyian and M. H. Al-Mallah, “Comparison of Machine Learning Techniques to Predict All-Cause Mortality using Fitness Data: The Henry Ford Exercise Testing (FIT) Project,” BMC Medical Informatics and Decision Making, vol. 17, no. 1, 2017.

C. M. Jones and T. Athanasiou, “SROC Analysis Techniques in the Evaluation of Diagnostic Tests,” Annals of Thoracic Surgery, vol. 79, pp. 16–20, 2005.

R. Couronne, P. Probst and A. Boulesteix, “Random Forest versus Logistic Regression: A Large-scale Benchmark Experiment,” BMC Bioinformatics, vol. 19, no. 270, 2018.

S. Zainudin, D. S. Jasim and A. A. Bakar, “Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction,” International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1148–1153, 2016.

F. E. Sapri, N. S. Nordin, S. M. Hasan, W. F. W. Yaacob and S. Azlin, “Decision Tree Model for Non-Fatal Road Accident Injury,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, pp. 63–70, 2017.




DOI: http://dx.doi.org/10.18517/ijaseit.9.5.7117

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