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A Comparative Analysis of Deep Learning Approaches for Predicting Breast Cancer Survivability

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Abstract

Breast cancer is the second largest cause of mortality among women. Breast cancer patients in developed nations have a relative survival rate of more than 5-years due to early detection and treatment. Deep learning approaches can help enhance the identification of breast cancer cells, lower the risk of detection mistakes, and minimize the time it takes to diagnose breast cancer using human methods. This paper examines the accuracy of artificial neural networks, Restricted Boltzmann Machine, Deep Autoencoders, and Convolutional Neural Networks (CNN) for post-operative survival analysis of breast cancer patients. A thorough examination of each network's operation and design is carried out to determine which network outperforms the other, followed by an analysis based on the network's prediction accurateness. The experimental results assert that all the deep learning techniques can predict the survival of breast cancer patients. The accuracy score achieved by Restricted Boltzmann Machine performed is the highest (0.97), followed by deep Autoencoders that attained an accuracy score of 0.96. CNN achieved a 92% accuracy score, while artificial neural networks attained the least accuracy score (0.89). The prediction performance of models has been evaluated using distinct parameters like accuracy, the area under the curve, F1 Score, Matthew’s correlation coefficient, sensitivity, and specificity. Also, the models have been validated using fivefold cross-validation techniques. However, there is still a need for complete analysis and research using deep learning methods to determine the design that provides superior accuracy.

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Data Availability

The datasets and Python code for data preprocessing and constructing deep learning models is publicly available online on Github (on request): https://github.com/surbhigupta24/Breast-Cancer-Survival-Prediction

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Acknowledgements

We express our gratitude to Dr. Rajeev Saini, MD, DNB (Medical Oncology), Narayana Multispecialty Clinic, Jammu & Kashmir (India), to provide us with consistent guidance on breast cancer. Dr. Saini has also assisted in confirming that the prediction indicators used in the study are extremely important from the medical point of view.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, Jammu and Kashmir, India

    Surbhi Gupta & Manoj K. Gupta

  2. Department of Computer Science and Engineering, Model Institute of Engineering & Technology, Jammu, J&K, India

    Surbhi Gupta

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  1. Surbhi Gupta
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  2. Manoj K. Gupta
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Correspondence to Surbhi Gupta.

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Gupta, S., Gupta, M.K. A Comparative Analysis of Deep Learning Approaches for Predicting Breast Cancer Survivability. Arch Computat Methods Eng 29, 2959–2975 (2022). https://doi.org/10.1007/s11831-021-09679-3

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