article · Big Data and Cognitive Computing
Breast cancer remains a widespread cancer affecting women globally, where early detection and precise classification significantly enhance survival rates. Manual diagnostic approaches demand extensive time and carry risks of human error. To improve survival prediction accuracy, a hybrid deep learning model combines information across multiple data sources, specifically clinical records, gene expression metrics, and copy number alteration data from the METABRIC dataset. The framework employs a convolutional neural network architecture for feature extraction, paired with Long Short-Term Memory and Gated Recurrent Unit networks as classifiers. Individually, these classifiers reach prediction accuracies of 97.0 percent and 97.5 percent respectively. Combining both classifiers through decision-level fusion achieves an overall accuracy of 98.0 percent, outperforming conventional single-modality methodologies across multiple standard evaluation metrics.
Accurate prediction of breast cancer patient survival helps medical professionals make better treatment decisions. Traditional manual assessment is labour-intensive and vulnerable to misclassification. By fusing diverse genetic and clinical datasets using artificial intelligence, computational models can deliver highly reliable prognostic predictions that support timely, personalised interventions.
This work demonstrates an algorithmic approach for clinical decision support systems that predict patient outcomes. The intended users are oncologists, pathologists, and clinical researchers requiring prognostic tools. Because the framework was evaluated solely on an academic retrospective dataset, the technology remains early-stage research and would need prospective validation and integration into clinical diagnostic software before reaching commercial adoption.
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Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the most prevalent types of cancer in women and is. Early breast cancer detection and classification are crucial. Early detection considerably increases the likelihood of survival, which motivates us to contribute to different detection techniques from a technical standpoint. Additionally, manual detection requires a lot of time and effort and carries the risk of pathologist error and inaccurate classification. To address these problems, in this study, a hybrid deep learning model that enables decision making based on data from multiple data sources is proposed and used with two different classifiers. By incorporating multi-omics data (clinical data, gene expression data, and copy number alteration data) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, the accuracy of patient survival predictions is expected to be improved relative to prediction utilizing only one modality of data. A convolutional neural network (CNN) architecture is used for feature extraction. LSTM and GRU are used as classifiers. The accuracy achieved by LSTM is 97.0%, and that achieved by GRU is 97.5, while using decision fusion (LSTM and GRU) achieves the best accuracy of 98.0%. The prediction performance assessed using various performance indicators demonstrates that our model outperforms currently used methodologies.
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DOI: 10.3390/bdcc7010050
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