Abstract

Cancer remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection and effective management. Predictive analytics, powered by machine learning (ML) and artificial intelligence (AI), offers a transformative potential in cancer care by integrating multi-source health data for early diagnosis, risk assessment, and personalized treatment. This presents a predictive analytics framework that leverages diverse data sources, including electronic health records (EHRs), genomic and biomarker data, medical imaging, wearable sensors, and lifestyle/environmental factors. By combining these heterogeneous data streams, AI models can identify high-risk individuals, detect cancer at its earliest stages, and optimize treatment strategies based on patient-specific insights. The framework employs advanced ML techniques, such as supervised learning models (Random Forests, Support Vector Machines), deep learning architectures (Convolutional Neural Networks, Recurrent Neural Networks), and Natural Language Processing (NLP) for analyzing clinical notes. Federated learning is also explored as a privacy-preserving approach to facilitate secure data sharing across institutions while maintaining compliance with regulatory standards such as HIPAA and GDPR. The integration of predictive analytics with telemedicine platforms and AI-driven clinical decision support systems further enhances real-time monitoring and personalized patient care. Despite its promise, challenges such as data privacy concerns, heterogeneity in multi-source data integration, model interpretability, and computational scalability must be addressed to ensure the effective implementation of AI-driven cancer diagnostics. Future advancements in Explainable AI (XAI), blockchain for secure data exchange, and multi-modal data fusion will further strengthen predictive analytics in oncology. This study underscores the critical role of big data and AI in revolutionizing cancer detection and management, paving the way for improved patient outcomes and more efficient healthcare delivery.