A genomic data analysis-based technique for personalized and precision medicine

Personalized and precision medicine are reshaping healthcare by tailoring treatment strategies to individual genetic, environmental, and lifestyle profiles. This study focuses on oncology and presents a machine learning framework that integrates genomic, clinical, demographic, and environmental data from ten common cancers to predict disease risk and recommend therapies. The framework combines random forests, support vector machines, and deep learning components. On internal 10-fold cross-validation, it achieved an overall prediction accuracy of 90.5% with an average inference time of 15 ms per sample. Paired statistical testing showed significant improvements over unified baseline models (p < 0.05). Fairness evaluation across demographic groups yielded demographic parity difference below 0.05 and equalized odds difference below 0.08. The drug recommendation module achieved 94.6% top-1 alignment with National Comprehensive Cancer Network (NCCN) recommendations. Cross-platform validation across four computational environments showed consistent performance. Compared with recent representative methods, the proposed framework achieved slightly higher accuracy with lower inference time. Shapley Additive Explanations (SHAP)-based analysis showed stable feature importance rankings across folds (average Spearman correlation > 0.85), supporting the robustness and interpretability of the model. These results indicate that integrating multi-modal patient information with machine learning can improve cancer risk prediction and support precision medicine decision-making.