PREDICTIVE MODELING FOR BLOOD CANCER DISEASE USING DEEP LEARNING ALGORITHM

Blood cancer, encompassing leukemia, lymphoma, and multiple myeloma, remains a major global health burden due to diagnostic challenges and complex biological behavior. Despite significant advancements in oncology, early detection remains elusive, particularly in low-resource settings. This study proposes the development of a predictive model using deep learning algorithms to enhance early blood cancer detection and classification. By leveraging structured datasets, electronic health records, and genomic data, the system aims to improve diagnostic accuracy, scalability, and accessibility. The study evaluates the performance of different deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble models against traditional diagnostic methods. The findings are expected to demonstrate that deep learning techniques can achieve superior predictive accuracy, thereby supporting clinicians in early diagnosis and treatment decision-making. Moreover, this research highlights the transformative potential of artificial intelligence in precision medicine, offering a scalable framework for real-time, data-driven cancer diagnostics. The integration of AI-based predictive systems in healthcare could significantly reduce diagnostic errors, enhance survival rates, and promote equitable access to advanced diagnostic tools across diverse populations.