CLASSIFICATION APPROACHES FOR MULTI-LEVEL BREAST CANCER PREDICTION: A MACHINE LEARNING PERSPECTIVE

Hujun Sun; Ling Weay Ang

doi:10.32350/jarms.31.03

Hujun Sun Malaysia University of Science and Technology
Ling Weay Ang

DOI: https://doi.org/10.32350/jarms.31.03

Keywords: Machine Learning, Deep Learning, Breast Cancer, Long Short-term Memory (LSTM), Recurrent Neural Networks

Abstract

Abstract Views: 19

This research compares the performance of machine learning and deep learning algorithms for breast cancer prediction in order to identify the most effective strategy for handling large data sets while maintaining high prediction accuracy. The study focuses on the use of machine intelligence techniques, such as machine learning and deep learning, for breast cancer prediction and classification. The paper reviews previous research on machine learning algorithms, segmentation techniques, and classification approaches used for multi-level breast cancer prediction. The results of the comparison show that using Long Short-Term Memory (LSTM) Recurrent Neural Networks for multi-disease prediction results in improved classification performance for breast cancer predictions.

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