Early Detection of Lung Cancer via Breath Analysis Utilising Electronic Nose

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2023-08-03 DOI:10.1109/icABCD59051.2023.10220490

Funmilayo S. Moninuola, E. Adetiba, Anthony A. Atayero, A. Awelewa, A. Adeyeye, Oluwadamilola Oshin, J. Ameh, A. Abayomi, Victor Ezekiel

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Abstract

Lung Cancer (LC), have the highest mortality rate and the second-highest incidence rate of all cancers combined because of a pathophysiological imbalance in the fundamental mechanism of cell proliferation. For patients with LC, prompt diagnosis and treatment are of utmost importance. The orthodox methods employed for detecting LC are characterised by invasiveness, protracted duration, high cost and exhibit reduced efficacy in detecting malignant cells during the initial phases of the ailment. The increasing attention of researchers toward the potential of utilising Volatile Organic Compound (VOC) biomarkers for the non-invasive detection of LC can be attributed to the advancements in techniques and procedures. This study offers a state-of-the-art portable E-nose that has the potential to enhance clinical outcomes associated with the early diagnosis of LC. Three ML models - SVM, AdaBoost, and MLP were employed to discriminate LC from other respiratory breathprint dataset. The MLP model achieved the highest performance accuracy result of 89.05%, specificity 95.12%, and sensitivity of 80%.

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利用电子鼻进行呼吸分析的肺癌早期检测

肺癌(LC)由于细胞增殖基本机制的病理生理失衡，在所有癌症中死亡率最高，发病率第二高。对于LC患者，及时诊断和治疗至关重要。传统的LC检测方法具有侵袭性、持续时间长、成本高、在疾病初期检测恶性细胞的效率较低等特点。研究人员越来越关注利用挥发性有机化合物(VOC)生物标志物进行LC无创检测的潜力，这可归因于技术和程序的进步。这项研究提供了一种最先进的便携式电子鼻，它有可能提高与LC早期诊断相关的临床结果。使用SVM、AdaBoost和MLP三种机器学习模型将LC与其他呼吸指纹数据进行区分。MLP模型的最高性能准确率为89.05%，特异性为95.12%，灵敏度为80%。

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

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.