References

Berner, E.S. and T.J. Lande, 2007. Overview of clinical decision support systems. Clinical Decision Support Systems, pp: 3-22

Devaraj, S. and S. Paulraj, 2015. An efficient feature subset selection algorithm for classification of multidimensional dataset. Sci. World J., 2015: 1-9.
Direct Link  |  

Garcia, S. and F. Herrera, 2008. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. J. Mach. Learn. Res., 9: 2677-2694.
Direct Link  |  

Guo, H. and S. Letourneau, 2013. Iterative classification for multiple target attributes. J. Intell. Inf. Sys., 40: 283-305.
CrossRef  |  Direct Link  |  

Kononenko, I., 2001. Machine learning for medical diagnosis: History, state of the art and perspective. Artif. Intell. Med., 23: 89-109.
PubMed  |  

Liu, G.P., G.Z. Li, Y.L. Wang and Y.Q. Wang, 2010. Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning. BMC. Complementary Altern. Med., 10: 1-12.
CrossRef  |  Direct Link  |  

Liu, G.P., J.J. Yan, Y.Q. Wang, W. Zheng and T. Zhong et al., 2014. Deep learning based syndrome diagnosis of chronic gastritis. Comput. Math. Methods Med., 2014: 1-8.

Palaniappan, S. and R. Awang, 2008. Intelligent heart disease prediction system using data mining techniques. Proceedings of the International Conference on Computer Systems and Applications, March 31-April 4, 2008, Doha, pp: 108-115.

Rokach, L., 2010. Ensemble-based classifiers. Artif. Intell. Rev., 33: 1-39.
CrossRef  |  Direct Link  |  

Rupnik, R. and M. Kukar, 2007. Decision support system to support decision processes with data mining. J. Inf. Organizational Sci., 31: 217-232.
Direct Link  |  

Senthilkumar, D. and S. Paulraj, 2013. Diabetes disease diagnosis using multivariate adaptive regression splines. International J. Eng. Technol., 5: 3922-3929.

Shortliffe, E.H. and G.O. Barnett, 2014. Biomedical Data: Their Acquisition, Storage and Use. In: Biomedical Informatics. Shortliffe, E.H. and J.J. Cimino (Eds.). Springer, London, England, ISBN: 978-1-4471-4474-8, pp: 39-66.

Wang, H., X. Liu, B. Lv, F. Yang and Y. Hong, 2014. Reliable multi-label learning via conformal predictor and random forest for syndrome differentiation of chronic fatigue in traditional Chinese medicine. PloS One, 9: 1-14.
Direct Link  |  

Wang, Y.Q., H.X. Yan, R. Guo, F.F. Li and C.M. Xia et al., 2011. Study on intelligent syndrome differentiation in Traditional Chinese Medicine based on multiple information fusion methods. Int. J. Data Min. Bioinf., 5: 369-382.

Xin, L. and Z. Feipeng and G. Yuhong, 2015. Conditional restricted boltzmann machines for multi-label learning with incomplete labels. Proc. AISTATS., 38: 635-643.

Zhao, Y. and L. He, 2014. Deep Learning in the EEG Diagnosis of Alzheimer's Disease. In: Asian Conference on Computer Vision. Jawahar, C.V. and S. Shan (Eds.). Springer International Publishing, Berlin, Germany, pp: 340-353.