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

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.

Application of association rule algorithm in intelligent manufacturing of mechanical manufacturing and automation specialty

“…Humans expect to carry out deeper big data analysis to make better use of these data, because this big data analysis already has the power to predict changes in the external environment, and this possibility is very large. The current database can efficiently realize data recording, searching, computing and other functions, but it cannot discover the relationships or laws existing in the data, and cannot predict the future development based on the existing data, because there is a lack of tools to extract the hidden data behind the data knowledge, leading to the phenomenon of "explosive growth of data and lack of knowledge" 11,12 . To sum up, there are many research results on association rule algorithm, but less research on application in manufacturing.…”