Using Big Data Analytics for Predicting Hospital Readmission Rates

Saurabh Gupta

Abstract: In the modern age, Health systems face several challenges, for instance readmissions to hospitals are most prominent among these challenges. To deal with this problem, numerous approaches and practices can produce desirable outcomes and reduce hospital readmissions. Using complicated and large volumes of health-related datasets is an ideal approach to tackle the performance constraints that arise due to readmissions. It is also valuable to produce such predictive health models that can offer early signs of several diseases so that required treatments may be initiated accordingly. Considering large datasets can be incorporated into a number of methodologies. This research paper explains these methodologies such as conventional statistical approaches, machine learning techniques as well as natural language processing (NLP). In the case of statistical approaches, the association between different datasets is found like clinical or socio-economic aspects. It also helps to calculate the risks of readmissions. Machine learning is more powerful in organizing nonlinear associations by considering large volumes of datasets that help to enhance the accuracy of predictive approaches. With the help of these expert methodologies, medical staff can make precise decisions to predict treatments to decrease the number of readmissions. This research elaborates on the advantages of analyzing dash datasets to bring advancements in the healthcare industry that eventually discourages readmissions. It also covers the expected challenges in the deployment of these practices including quality of data, interoperability plus data privacy.

Keywords: Big Data, Hospital Readmission, predictive health models

How to Cite?: Saurabh Gupta, "Using Big Data Analytics for Predicting Hospital Readmission Rates", Volume 15 Issue 1, January 2026, International Journal of Science and Research (IJSR), Pages: 95-99, https://www.ijsr.net/getabstract.php?paperid=SR251228103455, DOI: https://dx.doi.org/10.21275/SR251228103455