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In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiencies, improve patient quality of life, and perhaps most importantly, save the lives of more patients.
Data mining, a subfield of artificial intelligence that makes use of vast amounts of data in order to allow significant information to be extracted through previously unknown patterns, has been progressively applied in healthcare to assist clinical diagnoses and disease predictions [2].
Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases.
The data-mining process is divided into several steps: (1) database selection according to the research purpose; (2) data extraction and integration, including downloading the required data and combining data from multiple sources; (3) data cleaning and transformation, including removal of incorrect data, filling in missing data, generating new ...
The role of data mining in healthcare is vital as it enhances patient outcomes, supports evidence-based medicine, optimizes resource allocation, facilitates early disease detection, combats healthcare fraud, advances medical research, and promotes data-driven decision-making.
By harnessing the power of data mining, healthcare professionals can achieve greater precision, efficiency, and improved patient care. From tailored treatments to lower healthcare costs – data mining brings many benefits to clinicians, insurers, and patients.
In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016.