Accurate Integrated System to detect Pulmonary and Extra Pulmonary Tuberculosis using Machine Learning Algorithms

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence Pub Date : 1900-01-01 DOI:10.4114/intartif.vol24iss68pp104-122

R. Kaur, Anurag Sharma

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引用次数: 1

Abstract

Several studies have been reported the use of machine learning algorithms in the detection of Tuberculosis, but studies that discuss the detection of both types of TB, i.e., Pulmonary and Extra Pulmonary Tuberculosis, using machine learning algorithms are lacking. Therefore, an integrated system based on machine learning models has been proposed in this paper to assist doctors and radiologists in interpreting patients’ data to detect of PTB and EPTB. Three basic machine learning algorithms, Decision Tree, Naïve Bayes, SVM, have been used to predict and compare their performance. The clinical data and the image data are used as input to the models and these datasets have been collected from various hospitals of Jalandhar, Punjab, India. The dataset used to train the model comprises 200 patients’ data containing 90 PTB patients, 67 EPTB patients, and 43 patients having NO TB. The validation dataset contains 49 patients, which exhibited the best accuracy of 95% for classifying PTB and EPTB using Decision Tree, a machine learning algorithm.

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使用机器学习算法检测肺部和肺外结核的精确集成系统

已经有几项研究报道了在结核病检测中使用机器学习算法，但是讨论使用机器学习算法检测两种类型的结核病(即肺结核和肺外结核)的研究缺乏。因此，本文提出了一个基于机器学习模型的集成系统，以帮助医生和放射科医生解释患者的数据，以检测PTB和EPTB。三种基本的机器学习算法，决策树，Naïve贝叶斯，支持向量机，已经被用来预测和比较他们的性能。临床数据和图像数据被用作模型的输入，这些数据集是从印度旁遮普省贾朗达尔的各家医院收集的。用于训练模型的数据集包括200名患者的数据，其中包括90名PTB患者，67名EPTB患者和43名无TB患者。验证数据集包含49例患者，使用机器学习算法Decision Tree对PTB和EPTB进行分类的准确率达到95%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： Inteligencia Artificial is a quarterly journal promoted and sponsored by the Spanish Association for Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Particularly, the Journal welcomes: New approaches, techniques or methods to solve AI problems, which should include demonstrations of effectiveness oor improvement over existing methods. These demonstrations must be reproducible. Integration of different technologies or approaches to solve wide problems or belonging different areas. AI applications, which should describe in detail the problem or the scenario and the proposed solution, emphasizing its novelty and present a evaluation of the AI techniques that are applied. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs, surveys or special issues on topics, methods or techniques of special relevance to the AI community. Inteligencia Artificial welcomes submissions written in English, Spaninsh or Portuguese. But at least, a title, summary and keywords in english should be included in each contribution.