Abstract
The healthcare databases are coagulating with a poriferous rate, so assessing the varied parameters and detecting hidden patterns for future knowledge discovery is a complicated task. Data analytics has widely proven results in retrieving or extracting information from central databases in the past decade. It has created overall cognizance among the researchers and scientists worldwide to derive developmental technology for knowledge discovery in large scale databases. Indeed, healthcare databases have grown exponentially in volume and dimensionality with innovative IT-based technology for healthcare diagnosis. In healthcare databases, congenital heart disease is one of the foremost reasons for neonates’ deaths in develo** and developed countries. With the recent developments in the healthcare sector, possible treatments have emerged for curing congenital disabilities, which has resulted in an increased life span of patients with genetic disabilities. Thus, the affected persons can now live up to a higher age. The current study aimed to discover hidden patterns from congenital heart databases for future medical diagnosis using a clustering technique to find secret ways. The designs were analyzed as per the mortality rate of congenital heart defects at varying age groups in India between 2006–2016.
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Chauhan, R., Kaur, H. (2022). A Predictive Data Analytic Approach to Get Insight of Healthcare Databases. In: Acharjya, D.P., Mitra, A., Zaman, N. (eds) Deep Learning in Data Analytics. Studies in Big Data, vol 91. Springer, Cham. https://doi.org/10.1007/978-3-030-75855-4_8
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