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Multi-Class Neural Networks to Predict Lung Cancer

  • Patient Facing Systems
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Abstract

Lung Cancer is the leading cause of death among all the cancers’ in today’s world. The survival rate of the patients is 85% if the cancer can be diagnosed during Stage 1. Mining of the patient records can help in diagnosing cancer during Stage 1. Using a multi-class neural networks helps to identify the disease during its stage 1 itself. The implementation of multi-class neural networks has yielded an accuracy of 100%. The model created using the neural networks approach helps to identify lung cancer during Stage 1 itself, thus the survival rate of the patients can be increased. This model can serve as pre-diagnosis tool for the practitioners.

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Authors and Affiliations

  1. Sathyabama Institute of Science and Technology, Chennai, India

    Juliet Rani Rajan

  2. Department of Electronics and Instrumentation, RMD Engineering College, Chennai, India

    A. Chilambu Chelvan

  3. Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai, India

    J. Shiny Duela

Authors
  1. Juliet Rani Rajan
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  2. A. Chilambu Chelvan
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  3. J. Shiny Duela
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Correspondence to Juliet Rani Rajan.

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Rajan, J.R., Chelvan, A.C. & Duela, J.S. Multi-Class Neural Networks to Predict Lung Cancer. J Med Syst 43, 211 (2019). https://doi.org/10.1007/s10916-019-1355-9

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  • DOI: https://doi.org/10.1007/s10916-019-1355-9

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