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Recommender system: prediction/diagnosis of breast cancer using hybrid machine learning algorithm

Multimedia Tools and Applications volume 81, pages 9939–9948 (2022)Cite this article

Abstract

Breast cancer is the second popular cause of the women’s death. There are some existing techniques for identifying the breast cancer and one of them is mammography screening, for identifying the breast cancer under the age of 40 to 50 years. For medical diagnosis applications such as breast cancer, the recommender system is very helpful. There is a large number of records or datasets available for diagnosis of human diseases. In this article, we have presented an in-depth study of breast cancer predictions to take the remedial actions. Then experiments are carried out in Microsoft Azure on the breast cancer dataset which is available on Kaggle. The training and testing are done on 70% and 30% of the data. The evaluations are conducted by using machine learning algorithms, Locally Deep SVM, Boosted Decision Tree, Averaged Perception, Bayes Point and Decision Forest to predict Breast Cancer. We conducted an experiment on 18 K breast cancer image dataset. A hybrid machine learning algorithm (HMLA) based on decision tree and average perceptron algorithms is proposed. Based on the experimental evaluation, it is analyzed that proposed algorithm has performed well with 98.1% of accuracy and predicting the accurate results with 95.0% of sensitivity and specificity of 99.0% on the Breast Cancer prediction.

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Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Shalli Rani & Manpreet Kaur

  2. Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

Authors
  1. Shalli Rani
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  2. Manpreet Kaur
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  3. Munish Kumar
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Contributions

Shalli Rani (First Author): Investigation, Methodology, Writing - original draft, Data curation.

Manpreet Kaur (Second Author): Investigation, Methodology, Writing - original draft, Writing - review & editing, Data curation, Formal analysis, Investigation.

Munish Kumar (Third Author): Writing - review & editing, Formal analysis, Supervision.

Corresponding author

Correspondence to Munish Kumar.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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All the authors declared that they have no conflict of interest in this work.

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Rani, S., Kaur, M. & Kumar, M. Recommender system: prediction/diagnosis of breast cancer using hybrid machine learning algorithm. Multimed Tools Appl 81, 9939–9948 (2022). https://doi.org/10.1007/s11042-022-12144-3

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  • DOI: https://doi.org/10.1007/s11042-022-12144-3

Keywords

  • Recommender system
  • Collaborative filtering
  • Breast cancer
  • Machine learning
  • Benign and malignant stage