Abstract
Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.
Highlights
Childhood are assets of the nation that need good education and care for improving the quality of the nation [1]
In this paper we investigate the application of K-Nearest Neighbor (K-NN) with feature reduction to classify early childhood diseases
The results of the interview were information about how to classify early childhood diseases based on symptoms and complaints suffered by patients, and how to obtain patient data
Summary
Childhood are assets of the nation that need good education and care for improving the quality of the nation [1]. Improving children's health is one of the most important programs of the government. Data on early childhood disease collected in clinics has accumulated into big data Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods. Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases. Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%. The application of PCA and KNN can improve accuracy in the classification of early childhood diseases
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Information Systems Engineering and Business Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
