Background: Cancer remains one of the leading causes of mortality worldwide, with survival rates highly dependent on early detection. Conventional diagnostic strategies that rely on single-omics data, such as genomics or proteomics alone, often fail to capture the full complexity of tumor biology. Integrating multi-omics datasets—including genomics, transcriptomics, epigenomics, metabolomics, and proteomics—offers a systems-level perspective that can reveal hidden molecular interactions underlying cancer development. Advances in machine learning provide a powerful opportunity to harness these heterogeneous datasets for more accurate early detection and risk stratification.

Objective: This study develops a comprehensive machine learning framework for multi-omics data integration to improve early cancer detection. The framework addresses challenges of heterogeneity, scalability, and interpretability while seeking to enhance predictive accuracy and uncover clinically relevant molecular signatures.

Methods: The proposed pipeline includes five stages: (1) preprocessing and normalization of multi-omics datasets, (2) integration using advanced data fusion techniques, (3) feature selection and dimensionality reduction, (4) training of machine learning models such as Random Forest, gradient boosting, and deep learning architectures, and (5) risk stratification validated through survival analysis and cross-validation techniques.

Results: Expected outcomes include improved accuracy in patient classification, identification of novel biomarkers, and clearer stratification of individuals into clinically meaningful risk groups. Comparative performance assessments indicate that integrated multi-omics models outperform single-omics approaches in prediction tasks, yielding higher sensitivity and specificity.