Optimizing Machine Learning Algorithms for Cancer Detection

Modern biomedical research increasingly relies on high-throughput data generation and complex multi-omic datasets. The integration of chemical, biochemical, and genomic profiles into predictive modeling frameworks has significantly advanced disease diagnosis, prognosis, and therapeutic strategy optimization. Machine learning techniques have emerged as powerful tools for identifying subtle patterns in heterogeneous datasets. However, key challenges persist, including model interpretability, the effective integration of diverse data modalities, and the generalizability of models trained on limited or biased datasets. This study aims to develop improved machine-learning models to facilitate data-driven early cancer detection. The project will leverage chemical and biochemical data for early cancer detection. By employing data integration, advanced machine learning modeling, rigorous validation, and improved interpretability, this work seeks to provide robust computational tools for earlier and more accurate cancer diagnosis.