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Information retrieval using machine learning from breast cancer diagnosis

  • 1207: Innovations in Multimedia Information Processing & Retrieval
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Abstract

Breast cancer is one of the most common cancers among females. Patients with breast cancer are regularly rising. The survival of patients can be improved by early diagnosis and treatment. Because of its success, machine learning is commonly used in most fields. In this paper, numerous methods for early detection of this disease are employed for machine learning. Here, we consider C 5.0, Naive Bayes, logistic regression, random forest, ctree, KNN, K-Mean, GBM, adaBoost, decision tree model to classify the breast cancer tumor and evaluate their performances based on Wisconsin and SEER datasets. The demonstrations of the classifiers were assessed using accuracy, precision, recall, and F1 measure. We also predict whether the tumor is dead or alive, considering the tumor size, the various cancer stages, and months’ survival. The general research will increase people’s understanding of breast cancer and reduce tumor fears.

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Authors and Affiliations

  1. Division of Computer Engineering, Netaji Subhas University of Technology (Formerly Netaji Subhas Institute of Technology), New Delhi, India

    Deepti Singh

  2. Division of Computer Engineering, University of Delhi (Netaji Subhas Institute of Technology), New Delhi, India

    Ritu Nigam

  3. Department of Computer Science and Engineering, Ganga Institute of Technology and Management, Jhajjar, Haryana, India

    Ruchi Mittal

  4. Department of Computer Science & Engineering, Jaypee University, Sector 62 Noida, Uttar Pradesh, India

    Manju Nunia

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  1. Deepti Singh
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  2. Ritu Nigam
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Correspondence to Ruchi Mittal.

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Singh, D., Nigam, R., Mittal, R. et al. Information retrieval using machine learning from breast cancer diagnosis. Multimed Tools Appl 82, 8581–8602 (2023). https://doi.org/10.1007/s11042-022-13550-3

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  • DOI: https://doi.org/10.1007/s11042-022-13550-3

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