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Detailed Review on Breast Cancer Diagnosis Using Different ML Algorithms

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Data Engineering and Communication Technology

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 63))

Abstract

Breast cancer is the most prevalent cancer among Indian females with high mortality rate. It is reported that the incidence of breast cancer in India would reach upto 2 lakh per year by 2030. If breast cancer detected in early stages, it could be treated effectively resulting in decreased mortality. Machine learning is a specific field in artificial intelligence that utilizes variety of probabilistic techniques, statistical measures, optimization methods and allows systems to learn from past examples to discover patterns from large volumes of datasets. As a result, machine learning algorithms are used as efficient tools to diagnose and detect breast cancer. Since 80 s, there is an on-going research to diagnose and detect breast cancer using machine learning algorithms. This survey paper focuses on the overview of this research.

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Author information

Authors and Affiliations

  1. CSE Department, Osmania Univeristy, Hyderabad, India

    L. Vandana

  2. IT Department, CBIT, Hyderabad, India

    K. Radhika

Authors
  1. L. Vandana
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  2. K. Radhika
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Corresponding author

Correspondence to L. Vandana .

Editor information

Editors and Affiliations

  1. Kakatiya Institute of Technology and Science, Warangal, Telangana, India

    K. Ashoka Reddy

  2. Department of Electronics and Communication Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana, India

    B. Rama Devi

  3. Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India

    Boby George

  4. CMR Technical Campus, Hyderabad, Telangana, India

    K. Srujan Raju

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Vandana, L., Radhika, K. (2021). Detailed Review on Breast Cancer Diagnosis Using Different ML Algorithms. In: Reddy, K.A., Devi, B.R., George, B., Raju, K.S. (eds) Data Engineering and Communication Technology. Lecture Notes on Data Engineering and Communications Technologies, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-16-0081-4_52

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