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Evaluating the Performance of Machine Learning Techniques for Cancer Detection and Diagnosis

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Innovative Data Communication Technologies and Application (ICIDCA 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 46))

Abstract

Machine Learning (ML) techniques find value in healthcare due to its ability to process huge data sets and convert them into clinical insights. These insights help physicians and healthcare providers in planning, quick decision making and providing timely care to patients with higher accuracy, lower costs and increased customer satisfaction. This paper evaluates the performance of ML algorithms for cancer detection and diagnosis. It compares the performance of different cancer detection algorithms for different types of datasets. It also investigates how the performance of the ML algorithms improve with the use of feature extraction methods, specifically for lung cancer. Developing generic feature extraction methods still remain as a challenge. Healthcare providers need to reorient their treatment approach for diseases like cancer with more focus on detecting them at a very early stage so as to maximize the chances of recovery for the patients.

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Authors and Affiliations

  1. Department of Computer Science, CUSAT, Kochi, 682022, India

    Anu Maria Sebastian & David Peter

Authors
  1. Anu Maria Sebastian
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  2. David Peter
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Corresponding author

Correspondence to Anu Maria Sebastian .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, India

    Jennifer S. Raj

  2. College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi Arabia

    Abul Bashar

  3. Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, USA

    S. R. Jino Ramson

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Sebastian, A.M., Peter, D. (2020). Evaluating the Performance of Machine Learning Techniques for Cancer Detection and Diagnosis. In: Raj, J., Bashar, A., Ramson, S. (eds) Innovative Data Communication Technologies and Application. ICIDCA 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-030-38040-3_14

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