Harnessing Big Data and Predictive Analytics for Early Detection and Cost Optimization in Cancer Care

Abstract

Cancer continues to be a significant worldwide health concern, with increasing incidence and death imposing substantial expenses on patients, healthcare providers, and country economies (Bray et al., 2021; Sung et al., 2021). Conventional diagnostic and therapeutic approaches frequently do not identify cancers at an early, more manageable stage, while rising healthcare expenditures provide sustainability issues for both affluent and resource-limited healthcare systems (Mariotto et al., 2020; Yabroff et al., 2021). The emergence of big data and predictive analytics presents significant potential in tackling these dual concerns by facilitating earlier cancer detection and optimizing costs throughout the continuum of care. Big data in cancer includes diverse information sources such as electronic health records (EHRs), genomic databases, image archives, wearable devices, and insurance claims (Chen & Zhang, 2014; Raghupathi & Raghupathi, 2014). Predictive analytics, utilizing machine learning (ML), artificial intelligence (AI), and statistical modeling, enables doctors to discern concealed trends, anticipate illness progressions, and tailor treatment protocols (Obermeyer & Emanuel, 2016; Esteva et al., 2019). This study examines how the utilization of big data and predictive analytics might transform cancer care, emphasizing their functions in early detection and cost efficiency. The study conducts a thorough literature review and employs a methodological framework to analyze case examples from breast, lung, colorectal, and cervical cancers, illustrating the effectiveness of predictive models in early malignancy detection, minimizing late diagnoses, and facilitating cost-efficient interventions (Kourou et al., 2015; Cruz & Wishart, 2006; Topol, 2019). Moreover, it underscores the capacity of predictive modeling to reduce superfluous processes, optimize resource allocation, and enhance the value-based care framework (Yu et al., 2018; Rathore et al., 2017). Significant issues, such as data privacy, algorithmic bias, regulatory obstacles, and disparities in access, are examined to highlight the ethical and societal ramifications of this developing paradigm. The research contends that the incorporation of predictive analytics into oncology workflows is both a scientific need and an economic necessity, paving the way for precision oncology that improves survival rates while maintaining financial viability.