Machine Learning Models and Big Data Tools for Evaluating Kidney Acceptance

Abstract

The rise of on-demand healthcare and the unprecedented growth of electronic health records has given rise to big data opportunities and data analysis using machine learning. The massive and disparate data management using conventional databases is incredibly challenging and expensive to manage. It often requires specialized analytical tools for developing advanced data-driven capabilities and performing data analytics. This paper explores the capability of an open-source framework ‘Apache Spark’ capable of processing large amounts of data on clusters of nodes to analyze Big data and integrate technologies to provide decision support systems in healthcare settings. Next, we propose machine learning models on top of Apache Spark to expedite the decision-making in allocating organs such as kidney selection for the right candidate, thus increasing donor utilization by locating a recipient within the allotted time. The proposed models help in identifying waitlisted candidates willing to accept kidneys that may otherwise be discarded.

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