C4.5: programs for machine learning
C4.5: programs for machine learning
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Machine Learning
A study of applying data mining to early intervention for developmentally-delayed children
Expert Systems with Applications: An International Journal
Mining product maps for new product development
Expert Systems with Applications: An International Journal
A case study of applying data mining techniques in an outfitter's customer value analysis
Expert Systems with Applications: An International Journal
An expert system to predict protein thermostability using decision tree
Expert Systems with Applications: An International Journal
Efficient sleep spindle detection algorithm with decision tree
Expert Systems with Applications: An International Journal
Automatic classification of Tamil documents using vector space model and artificial neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Artificial neural networks for optimization of gold-bearing slime smelting
Expert Systems with Applications: An International Journal
Data mining approach for supply unbalance detection in induction motor
Expert Systems with Applications: An International Journal
Applying data mining to explore the risk factors of parenting stress
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Preterm birth is the leading cause of perinatal morbidity and mortality, but a precise mechanism is still unknown. Hence, the goal of this study is to explore the risk factors of preterm using data mining with neural network and decision tree C5.0. The original medical data were collected from a prospective pregnancy cohort by a professional research group in National Taiwan University. Using the nest case-control study design, a total of 910 mother-child dyads were recruited from 14,551 in the original data. Thousands of variables are examined in this data including basic characteristics, medical history, environment, and occupation factors of parents, and variables related to infants. The results indicate that multiple birth, hemorrhage during pregnancy, age, disease, previous preterm history, body weight before pregnancy and height of pregnant women, and paternal life style risk factors related to drinking and smoking are the important risk factors of preterm birth. Hence, the findings of our study will be useful for parents, medical staff, and public health workers in attempting to detect high risk pregnant women and provide intervention early to reduce and prevent preterm birth.