Using Data Mining to Improve Healthcare Decision-Making: A Systematic Review

Abstract

Data mining is a process utilized to analyze large databases to find patterns and relationships. It involves using various techniques such as machine learning and statistical analysis to identify trends and patterns. Due to the massive amount of medical data that is available, data mining has become an increasing trend in healthcare. This process involves extracting knowledge from these data sources. This review aims to analyze the current state of the research regarding the use of data mining in the healthcare industry. This review thoroughly searched for studies related to various aspects of healthcare diagnosis and prognosis. The results indicated that data mining techniques can help improve the accuracy of diagnoses and the prediction of treatment outcomes.  Although the review acknowledged the potential of data mining in improving the quality of healthcare, it also highlighted various issues that need to be resolved in order for it to be successful. These include the need for standardization and privacy concerns. Despite these obstacles, the potential of this technology is still immense. The findings of this review provide a comprehensive analysis of the current state of the art in data mining in healthcare. It shows the potential of this technology to improve the quality of healthcare and inform clinical decisions. Future research must address the issues identified in this review in order to advance the use of this technology in the field.