Tuberculosis is ranked as the 2nd deadliest disease in the world and is responsible for ten million deaths in 2017. Treatment failure is one of a main reason behind these deaths. Reasons of treatment failure are still unknown and the death rate due to TB is increasing. Machine learning and data analytics approaches are proved to be useful in healthcare domain in finding the associations among different attributes that can affect the outcome of any disease. Timely identification of reasons can save a patient's life. This study aims to find features that are strongly correlated with treatment failure using feature selection techniques. The validation of features is demonstrated using different classification algorithms. Moreover, this study provides a demographic based feature association of six highly burdened treatment failure countries. A verified real-life patient's dataset gathered from different countries including Azerbaijan, Belarus, Georgia, India, Moldova, and Romania is utilized to address the problem. Two types of experimentation are performed on combined dataset by achieving an average accuracy of 78% and an accuracy of 92% on Romania's data. Results shows the importance of features obtained through this study are highly influential in leading a patient towards treatment failure.

中文翻译：

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基于机器学习的结核病治疗失败预测框架：六个国家的案例研究

结核病被列为世界上第二大致命疾病，2017 年造成 1000 万人死亡。治疗失败是造成这些死亡的主要原因之一。治疗失败的原因仍然未知，结核病死亡率正在上升。事实证明，机器学习和数据分析方法在医疗保健领域中非常有用，可用于发现可能影响任何疾病结果的不同属性之间的关联。及时查明原因可以挽救患者的生命。本研究旨在使用特征选择技术找到与治疗失败密切相关的特征。使用不同的分类算法演示了特征的验证。此外，这项研究提供了六个高负担治疗失败国家的基于人口统计学的特征关联。从包括阿塞拜疆、白俄罗斯、格鲁吉亚、印度、摩尔多瓦和罗马尼亚在内的不同国家收集的经过验证的真实患者数据集用于解决该问题。通过在罗马尼亚数据上实现 78% 的平均准确度和 92% 的准确度，对组合数据集进行了两种类型的实验。结果表明，通过这项研究获得的特征的重要性在导致患者走向治疗失败方面具有很大影响。