Publication Type : Journal Article
Publisher : IETE Journal of Research, Taylor & Francis
Source : IETE Journal of Research, Taylor & Francis (2021)
Url : https://www.tandfonline.com/doi/abs/10.1080/03772063.2021.1923076?journalCode=tijr20
Campus : Chennai
School : School of Engineering
Department : Computer Science
Year : 2021
Abstract : Recognizing cardiac patients with high risk of hospitalization could enable to offer timely and life-saving care. The accumulation of healthcare data and utilization of data analytics to develop risk prediction models from healthcare data could facilitate personalized treatment care and predict the risk of emergency. Healthcare providers use different prediction tools to improve clinical decision making as there is a relation between hospitalization and disease diagnosis, disease complications and disease treatment. Several factors constitute to the hospitalization of cardiac patients such as age, gender, disease type, disease complication, associated disease conditions and so on. In this paper, a prediction model is developed to predict the risk of hospitalization of cardiac patients and the significance of each factor that contributes to the risk of hospitalization of cardiac patients is measured. The proposed model is designed to discover and validate the factors that are associated with the high risk of hospitalization in cardiac patients.
Cite this Research Publication : M. Chandralekha, N. Shenbagavadivu (2021): "Data Analytics for Risk of Hospitalization of Cardiac Patients", IETE Journal of Research, Taylor & Francis, DOI: 10.1080/03772063.2021.1923076.