Evaluation and Comparison of Different Machine Learning Methods to Predict Outcome of Tuberculosis Treatment Course

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Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
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"Evaluation and Comparison of Different Machine Learning Methods to Predict Outcome of Tuberculosis Treatment Course"
written by Sharareh R. Niakan Kalhori, Xiao-Jun Zeng,
published by Journal of Intelligent Learning Systems and Applications, Vol.5 No.3, 2013

has been cited by the following article(s):

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[1]	Clinical Pathways and Hierarchical Clustering for Tuberculosis Treatment Outcome Prediction Procedia Computer Science, 2023 DOI:10.1016/j.procs.2023.01.425

[2]	A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district PLOS Global Public Health, 2023 DOI:10.1371/journal.pgph.0001466

[3]	Clinical Pathways and Hierarchical Clustering for Tuberculosis Treatment Outcome Prediction Procedia Computer Science, 2023 DOI:10.1016/j.procs.2023.01.425

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[5]	Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis Informatics, 2021 DOI:10.3390/informatics8020027

[6]	The impact of oversampling with “ubSMOTE” on the performance of machine learning classifiers in prediction of catastrophic health expenditures Operations Research for Health Care, 2020 DOI:10.1016/j.orhc.2020.100275

[7]	Bayesian Spatial Survival Analysis of Duration to Cure among New Smear-Positive Pulmonary Tuberculosis (PTB) Patients in Iran, during 2011–2018 International Journal of Environmental Research and Public Health, 2020 DOI:10.3390/ijerph18010054

[8]	The impact of oversampling with “ubSMOTE” on the performance of machine learning classifiers in prediction of catastrophic health expenditures Operations Research for Health Care, 2020 DOI:10.1016/j.orhc.2020.100275

[9]	Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 DOI:10.1145/3292500.3330777

[10]	Prediction of Coronary Heart Disease using Machine Learning Proceedings of the 2019 3rd International Conference on Deep Learning Technologies, 2019 DOI:10.1145/3342999.3343015

[11]	Prediction of Coronary Heart Disease using Machine Learning Proceedings of the 2019 3rd International Conference on Deep Learning Technologies, 2019 DOI:10.1145/3342999.3343015

[12]	Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 DOI:10.1145/3292500.3330777

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