Breast Cancer Classification Using Gene Expression Profile Data with a Deep Learning Framework
DOI:
https://doi.org/10.21015/vtse.v13i4.2255Abstract
Breast cancer is a harmful disease that is dangerous to human life. It causes a continuous increase in the overall death rate. It is a challenging task for machine learning models to classify gene expression profile data because of its complex nature. These machine-learning models took a lot of time and consumed larger data during their training, which resulted in inconsistent accuracy. The main purpose of this research is to build a deep neural network model that can accurately classify cancerous and non-cancerous patients by using gene expression profile datasets of breast cancer. This study presents a novel deep learning framework specifically designed for the classification of breast cancer using gene expression profile data. Unlike conventional machine learning approaches that require extensive preprocessing and manual feature extraction, the proposed model automatically learns discriminative features from high-dimensional genomic data. The proposed research implies a highly accurate model having precision (0.91), recall (1.00), and F1 score (0.95) with 95% accuracy. The macro average and weighted average both indicate that the model performs well on average also which is helpful for medical practitioners for early breast cancer prognosis.
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