K-Nearest Neighbor Based Association Data Mining in Healthcare Correlated Data Systems

Abstract

Indeed, data mining techniques have been used to find hidden patterns and linkages in health-related practices, summarize data in unique ways that are both understandable to healthcare stakeholders, and predict future patterns and behaviors. Various data mining strategies and approaches have gotten a lot of attention and research. Medical information has progressed in the direction of intelligence as a result of the rapid advancement of information technology. The K-nearest neighbor classification algorithm is frequently utilized in various disciplines due to its simplicity. When the sample size is high and the feature attributes are substantial, the K - nearest neighbor algorithm classification efficiency has also grown greatly. This study demonstrates how a K-nearest neighbor- based data mining technique was used to index data and analyze a clinical data set from an outpatient facility. As a result, the experimental findings suggest that the proposed technique can significantly increase the KNN algorithm's classification efficiency when processing a huge data set. Using the K-nearest neighbor algorithm, data mining techniques may classify customer behavior depending on the prospect, responder, active, and other entities of the customer's life cycle.