当前位置: X-MOL 学术Int. J. Inf. Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Drug resistant tuberculosis classification using logistic regression
International Journal of Information Technology Pub Date : 2021-01-03 , DOI: 10.1007/s41870-020-00592-9
Odu Nkiruka Bridget , Rajesh Prasad , Clement Onime , Adamu Abubakar Ali

Tuberculosis (TB) is a communicable disease that poses a serious threat to the lives of people in the world. TB may be classified as drug-sensitive (DS-TB) or drug resistant (DR-TB). Clinical methods for diagnosing DR-TB require costly infrastructure as well as highly skilled expertise and also have major limitations due to the large amount of time taken to provide results. Researchers in the past have applied some machine learning methods in diagnosing TB, but not many studies have considered DR-TB and proved much satisfactory results. This paper examines the classification of DR-TB, based on existing symptoms, using logistic regression (LR) which is well known for its application in predictive diagnosis of diseases, and such approach is advantageous because they allow a quick evaluation and classification of disease outcomes in the presence of multiple and complex symptoms. To ensure that the symptoms selected have a direct relationship with DR-TB, a statistical test of hypothesis was established to prove the degree of association between the symptoms and DR-TB. In comparison to other techniques, the key advantages of the proposed LR classifier are its ability to show the degree to which a predictor variable affects the target variable and it works well with fewer feature variables. The comparative results with other machine learning techniques shows that LR classifier resulted in more accuracy score than others.



中文翻译:

使用Logistic回归对耐药性肺结核进行分类

结核病是一种传染性疾病,对世界人民的生命构成严重威胁。结核病可分为药物敏感性(DS-TB)或耐药性(DR-TB)。诊断DR-TB的临床方法需要昂贵的基础设施以及高度熟练的专业知识,并且由于要花费大量时间来提供结果,因此也存在重大局限性。过去,研究人员已将某些机器学习方法应用于结核病的诊断,但很少有研究考虑过DR-TB,并证明了令人满意的结果。本文使用现有的Logistic回归(LR)方法,根据现有症状对DR-TB的分类进行了研究,该模型以其在疾病的预测诊断中的应用而著称,并且这种方法是有利的,因为它们允许在存在多种复杂症状的情况下快速评估疾病结果并对其进行分类。为了确保所选症状与DR-TB有直接关系,建立了假设的统计检验以证明症状与DR-TB之间的关联程度。与其他技术相比,拟议的LR分类器的主要优势在于它能够显示预测变量对目标变量的影响程度,并且能够以较少的特征变量很好地工作。与其他机器学习技术的比较结果表明,LR分类器比其他分类器产生的准确性得分更高。建立假设的统计检验以证明症状与DR-TB之间的关联程度。与其他技术相比,拟议的LR分类器的主要优势在于它能够显示预测变量对目标变量的影响程度,并且可以在较少的特征变量的情况下很好地工作。与其他机器学习技术的比较结果表明,LR分类器比其他分类器产生的准确性得分更高。建立假设的统计检验以证明症状与DR-TB之间的关联程度。与其他技术相比,拟议的LR分类器的主要优势在于它能够显示预测变量对目标变量的影响程度,并且能够以较少的特征变量很好地工作。与其他机器学习技术的比较结果表明,LR分类器比其他分类器产生的准确性得分更高。

更新日期:2021-01-03
down
wechat
bug