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

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  • Kanchan Pradhan
  • Priyanka Chawla

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.

Suggested Citation

  • Kanchan Pradhan & Priyanka Chawla, 2020. "Medical Internet of things using machine learning algorithms for lung cancer detection," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(4), pages 591-623, October.
    • Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:591-623
    DOI: 10.1080/23270012.2020.1811789
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    Cited by:

    1. Borch, Christian, 2022. "Machine learning, knowledge risk, and principal-agent problems in automated trading," Technology in Society, Elsevier, vol. 68(C).
    2. Weifeng Jia & Shuo Wang & Yongping Xie & Zifeng Chen & Kaixin Gong, 2022. "Disruptive technology identification of intelligent logistics robots in AIoT industry: Based on attributes and functions analysis," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 557-568, May.
    3. Yu Sun & Yuming He & Haiqing Yu & Hecheng Wang, 2022. "An evaluation framework of IT‐enabled service‐oriented manufacturing," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 657-667, May.
    4. Hung Viet Nguyen & Haewon Byeon, 2023. "Prediction of ECOG Performance Status of Lung Cancer Patients Using LIME-Based Machine Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    5. Wei Zhang & Linhui Sun & Xinping Wang & Anbo Wu, 2022. "The influence of AI word‐of‐mouth system on consumers' purchase behaviour: The mediating effect of risk perception," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 516-530, May.

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