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Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

Author

Listed:
  • Vijayalakshmi S
  • John A
  • Sunder R
  • Senthilkumar Mohan
  • Sweta Bhattacharya
  • Rajesh Kaluri
  • Guang Feng
  • Usman Tariq

Abstract

Cancer is enlisted as the second leading reason for death across the world wherein almost one person out of six dies of cancer. Breast cancer is one of the most common forms of cancer predominant in women having the second highest mortality rate in the world. Various scientific studies have been conducted to combat this disease, and machine learning approaches have been an extremely popular choice. Particle swarm optimization has been identified as one of the most powerful and efficient technique for the diagnosis of breast cancer guiding physicians towards timely and accurate treatment. It is also pertinent to mention that multi-modal prediction methods are used to make decisions depending upon different scenarios and aspects whereas the non-dominating sorting feature is useful to sort different objects based on differing requirements. The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. The work encompasses the use of particle swarm optimization, non-dominating sorting and multi-classifier techniques, namely, k -nearest neighbour method, fast decision tree and kernel density estimation. Finally, Bayes’ theorem is implemented for revising the results to achieve optimum accuracy in the breast cancer prediction. The proposed particle swarm optimization and non-domination sorting with classifier technique model helps to select the most significant features relevant to breast cancer predictions. The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. The metrics considered are sensitivity, specificity, accuracy and time complexity. The experimental results of the study using measures such as sensitivity, specificity, accuracy and time complexity. The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.

Suggested Citation

  • Vijayalakshmi S & John A & Sunder R & Senthilkumar Mohan & Sweta Bhattacharya & Rajesh Kaluri & Guang Feng & Usman Tariq, 2020. "Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:11:p:1550147720971505
    DOI: 10.1177/1550147720971505
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    References listed on IDEAS

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    1. Rau Andrea & Jaffrézic Florence & Foulley Jean-Louis & Doerge Rebecca W, 2010. "An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-28, January.
    2. Harshita Patel & Dharmendra Singh Rajput & G Thippa Reddy & Celestine Iwendi & Ali Kashif Bashir & Ohyun Jo, 2020. "A review on classification of imbalanced data for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.
    3. Olvi L. Mangasarian & W. Nick Street & William H. Wolberg, 1995. "Breast Cancer Diagnosis and Prognosis Via Linear Programming," Operations Research, INFORMS, vol. 43(4), pages 570-577, August.
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    Cited by:

    1. Yanfeng Wang & Haohao Wang & Sanyi Li & Lidong Wang, 2022. "Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 10(9), pages 1-20, April.

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