Big data analytics in predictive nursing: Leveraging machine learning for early disease detection

Omoregie, David Thomas (2025) Big data analytics in predictive nursing: Leveraging machine learning for early disease detection. International Journal of Science and Research Archive, 15 (1). pp. 1052-1059. ISSN 2582-8185

Integration of big data analytics has become a crucial area in the development of the nursing sector. This has completely changed the way diseases are predicted. The traditional methods have been effective but they often times have problems like delayed diagnosis and increased patient mortality. This study then shows models that improves on the traditional methods and applies them to large scale healthcare datasets. This enhances the accuracy and efficiency of early disease prediction. The data used in this project was sourced from electronic health records (EHRs), wearable IoT devices, genomic data, and medical imaging. The research then evaluates various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines (SVM), XGBoost, and Deep Neural Networks (DNN). The models were tested on a dataset of 1,500 patient records, and XGBoost achieved the highest predictive accuracy (91.5%). The findings highlight the significant advantages such as reducing misdiagnosis, enabling real-time health monitoring, and optimizing patient care strategies. However it is not without its challenges. These challenges include data privacy and model interpretability. This must be addressed for broader clinical adoption. The study also provides meaningful recommendations for integrating AI into the system. This will of course increase efficiency and effectiveness.