Stacked Deep Learning Ensemble for Multiomics Cancer Type Classification: Development and Validation Study.

Background: Cancer is one of the leading causes of disease burden globally, and early and accurate diagnosis is crucial for effective treatment. This study presents a deep learning-based model designed to classify 5 common types of cancer in Saudi Arabia: breast, colorectal, thyroid, non-Hodgkin lymphoma, and corpus uteri. Objective: This study aimed to evaluate whether integrating RNA sequencing, somatic mutation, and DNA methylation profiles within a stacking deep learning ensemble improves cancer type classification accuracy relative to the current state-of-the-art multiomics models. Methods: Using a stacking ensemble learning approach, our model integrates 5 well-established methods: support vector machine, k-nearest neighbors, artificial neural network, convolutional neural network, and random forest. The methodology involves 2 main stages: data preprocessing (including normalization and feature extraction) and ensemble stacking classification. We prepared the data before applying the stacking model. Results: The stacking ensemble model achieved 98% accuracy with multiomics versus 96% using RNA sequencing and methylation individually, 81% using somatic mutation data, suggesting that multiomics data can be used for diagnosis in primary care settings. The models used in ensemble learning are among the most widely used in cancer classification research. Their prevalent use in previous studies underscores their effectiveness and flexibility, enhancing the performance of multiomics data integration. Conclusions: This study highlights the importance of advanced machine learning techniques in improving cancer detection and prognosis, contributing valuable insights by applying ensemble learning to integrate multiomics data for more effective cancer classification.