Earticle

초록

영어

Due to the simplicity of the k-nearest neighbor classification algorithm, it has been widely used in many fields. Until now, when the sample size is enormous and the feature attributes are outsized, the productivity of the k-nearest neighbor algorithm classification has also significantly increased. This work demonstrates that a k-nearest neighbor-based data mining technique has been utilized for data index to gather data and analyze an outpatient facility's clinical data set. Therefore, the investigational results show that the suggested algorithm can effectively improve the classification effectiveness of the KNN algorithm in processing a large set of data. Data extraction and fetching techniques can classify possible user/customer behavior using the k-nearest neighbor algorithm based on the user or consumer's impression, entailing prospects, responders, active entities, and different entities. Data mining methods have been utilized to uncover undisclosed patterns and relations. Undoubtedly, the information in a novel manner is reasonable to the healthcare stakeholders and to anticipate future patterns and practices in health-related practices. Many examinations and work have focused on various data mining strategies and approaches. The advanced growth of data science, information, and communication technology has directed the progress of medical-based details toward new artificial intelligence-based processes and data sciences.

목차

Ⅰ. INTRODUCTION
Ⅱ. Classification and Association of DataMining Requirements in CorrelatedSystems
Ⅲ. Tendency of Analysis and Regression inHealthcare-Correlated Systems
Ⅳ. K- Nearest Neighbor Method Diagnosis andPredictions in Correlated Systems
V. k-Nearest Neighbor Analysis in DataScheming
Ⅵ. Conclusion
References

저자

• Murtaza Hussain Shaikh [ Assistant Professor, Kyungsung University ]
• Kim Yae-Ji [ Assistant Professor, Dongeui University ] Corresponding author

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