Medical Internet of things using machine learning algorithms for lung cancer detection

Kanchan Pradhan and Priyanka Chawla

Journal of Management Analytics, 2020, vol. 7, issue 4, 591-623

Abstract: This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for predicting different diseases, using machine learning algorithms, has been done. The analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical IoT. Each technique was analyzed on each step, and the overall drawbacks are pointed out. In addition, it also analyzes the type of data used for predicting the concerned disease, whether it is the benchmark or manually collected data. Finally, research directions have been identified and depicted based on the various existing methodologies. This will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/23270012.2020.1811789 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:591-623

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjma20

DOI: 10.1080/23270012.2020.1811789

Access Statistics for this article

Journal of Management Analytics is currently edited by Li Xu

More articles in Journal of Management Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().