<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.</p></abstract>

This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Information System Study Program, Department of Mathematics

Statistics Study Program, Department of Mathematics

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was...

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Online veröffentlicht: 26. Sept. 2019

Seitenbereich: 154 - 167

Eingereicht: 04. Jan. 2019

Akzeptiert: 07. Juni 2019

DOI: https://doi.org/10.2478/cait-2019-0031

Schlüsselwörter
Early childhood diseases, PCA, Medical record, Apriori Algorithm

© 2019 Indah Werdiningsih et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Online veröffentlicht: 26. Sept. 2019

Seitenbereich: 154 - 167

Eingereicht: 04. Jan. 2019

Akzeptiert: 07. Juni 2019

DOI: https://doi.org/10.2478/cait-2019-0031

SchlüsselwörterEarly childhood diseases, PCA, Medical record, Apriori Algorithm

© 2019 Indah Werdiningsih et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Early childhood diseases, PCA, Medical record, Apriori Algorithm