Integrating Machine Learning and Big Data Analytics for Early Disease Detection in U.S. Health Systems
Keywords:
Big Data Analytics, Early Disease Detection, Machine Learning, Multi-modal Data, Predictive ModellingAbstract
This systematic review article explores the use of machine learning and big data analytics in the early disease detection system in the U.S. healthcare systems. The objectives of the study are to know what is being done, assess the level of predictive performance and what are the implementation challenges and enablers. In the search of four large databases over the period of 2014 to 2024, 11 studies that met the rigid inclusion criteria were found in the U.S. setting, clinical validation. The results show that machine learning algorithms have high accuracy of over 90%, and significant success has been demonstrated in neurological, metabolic, and infectious diseases. Predictive performance is improved with multi-modal data integration with imaging, genetic and electronic health record data. Although this delivery of technical results showed that there are very great barriers to translate models into the normal workings of the clinic (data quality, interoperability, model interpretability, absence of external validation, etc.). The majority of the models are at the stage of proving the concept, and this fact creates a significant distance between the development and the practical use. Other acute gaps in patient diversity representation, long-term outcome connections, infrastructure preparedness are also identified in the review. In order to achieve machine learning to its maximum capacity in early diagnosis, one will need to invest in data governance, interdisciplinary implementation teams and continuous monitoring of models. The review identifies that machine learning is a transformative opportunity to proactive healthcare, and strategy should shift toward implementation science, external validation, and equitable use to gain meaningful clinical impact.
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Copyright (c) 2026 Daniel Abaneme, Benita Chinemerem, Yusuf Kolawole Adebakin, Oladapo Omobayo Aiyenitaju, Albert Darko
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