Journal of Intelligent Learning Systems and Applications, 2013, 5, 184-193 http://dx.doi.org/10.4236/jilsa.2013.53020 Published Online August 2013 (http://www.scirp.org/journal/jilsa)
Evaluation and Comparison of Different Machine Learning Methods to Predict Outcome of Tuberculosis Treatment Course Sharareh R. Niakan Kalhori1,2*, Xiao-Jun Zeng3 1 Department of Public Health, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 2Social Determinants of Health Research Center, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 3Department of Machine Learning & Optimization, School of Computer Science, University of Manchester, Manchester, England. Email: *
[email protected]
Received December 24th, 2012; revised January 24th, 2013; accepted February 4th, 2013 Copyright © 2013 Sharareh R. Niakan Kalhori, Xiao-Jun Zeng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT Tuberculosis treatment course completion is crucial to protect patients against prolonged infectiousness, relapse, lengthened and more expensive therapy due to multidrug resistance TB. Up to 50% of all patients do not complete treatment course. To solve this problem, TB treatment with patient supervision and support as an element of the “global plan to stop TB” was considered by the World Health Organization. The plan may require a model to predict the outcome of DOTS therapy; then, this tool may be used to determine how intensive the level of providing services and supports should be. This work applied and compared machine learning techniques initially to predict the outcome of TB therapy. After feature analysis, models by six algorithms including decision tree (DT), artificial neural network (ANN), logistic regression (LR), radial basis function (RBF), Bayesian networks (BN), and support vector machine (SVM) developed and validated. Data of training (N = 4515) and testing (N = 1935) sets were applied and models evaluated by prediction accuracy, F-measure and recall. Seventeen significantly correlated features were identified (P 1) denote that our variables are far from normality and decision tree and other symbolic methods (nonparametric JILSA
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schemes) tackle robustly with distributions with large kurtosis and skew [22]. In this research’s dataset, the average values of skew and kurtosis are 2.169 and 7.469 respectively (P < 0.05) confirming the non-normal distribution. Hence, the only available nonparametric symbolic learning algorithm in the current study is decision tree which performed well to partition the input space. In actuality, high skew (>1) or kurtosis (>7) along with the presence of binary/categorical variables, using relevant and correlated predictors without any missed instances or noised data have prepared the best opportunity for decision tree to predict more accurately than other applied algorithms. In view of the rank of other employed classifiers, BN outperforms others and four remaining classifiers work relatively similar with prediction accuracy percentage ranging from 53.74% to 57.82%. In a study [24], Tu has reviewed a number of researches concluding that LR & MLP perform closely; it is the case here where they performed with identical prediction accuracy (57.82). RBF is actually a type of neural network and it might be a postulation that based on their algorithm similarities and data type entity results are comparable. Decision tree with flowchart-type structure is more likely method to be understandable for general users with low level of specialized knowledge about TB. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood either by doctors or health practitioners who implement DOTS in rural area and make decision alone.
no conflict of interest.
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5. Conclusion To sum up, available big body of real data related to TB patients has created an opportunity to generate accurate models which can predict outcome of DOTS therapy. This provides us information about outcome of treatment course for each patient and defines who needs high level of supervision and support; this is valuable as it is not possible to give every single of patients a full supervision and support distinctly. The decision tree model can be used to screen risky patients for fail in treatment course completion in population using general data gathering in routine general practice. This will help healthcare practitioners especially in rural regions to evaluate the risks of MDR-TB among their patients quickly, inexpensively, and noninvasively. TB control through totally implemented DOTS therapy is such a crucial stage in public health improvement and promotion.
6. Acknowledgements We would like to express our gratitude to Iranian Ministry of Health and Medical Education for funding and data access. Furthermore, the authors declare that they have Copyright © 2013 SciRes.
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