  The personalized health care service utilizes the relational patient data and
big data analytics to tailor the medication recommendations. However, most of
the health care data are in unstructured form and it consumes a lot of time and
effort to pull them into relational form. This study proposes a novel data lake
architecture to reduce the data ingestion time and improve the precision of
healthcare analytics. It also removes the data silos and enhances the analytics
by allowing the connectivity to the third-party data providers (such as
clinical lab results, chemist, insurance company,etc.). The data lake
architecture uses the Hadoop Distributed File System (HDFS) to provide the
storage for both structured and unstructured data. This study uses K-means
clustering algorithm to find the patient clusters with similar health
conditions. Subsequently, it employs a support vector machine to find the most
successful healthcare recommendations for the each cluster. Our experiment
results demonstrate the ability of data lake to reduce the time for ingesting
data from various data vendors regardless of its format. Moreover, it is
evident that the data lake poses the potential to generate clusters of patients
more precisely than the existing approaches. It is obvious that the data lake
provides a unified storage location for the data in its native format. It can
also improve the personalized healthcare medication recommendations by removing
the data silos.
