Open AccessPreterm Birth Prediction using Hybrid Ant Colony-Genetic Optimization Algorithm in Data Mining
Author(s) -
Maher S. Mohamed,
P. Mayilvahanan
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8832.019320
Subject(s) - ant colony optimization algorithms , birth weight , genetic algorithm , medicine , computer science , obstetrics , pregnancy , demography , algorithm , machine learning , biology , sociology , genetics
Many data mining (DM) methods are used to explore the risk factors of Preterm birth (PTB) and to predict preterm birth. High rates of infant mortality, preterm births and maternal mobility and continuous variation in pregnancy outcomes are an important public health issue in India and worldwide. In this paper, aims to develop and evaluate prevent factors of preterm birth using hybrid algorithm which is optimized the model with genetic algorithm and ant bee colony algorithm based also analysis of risk factor of preterm birth prediction. It is identified that variables which were highly influenced to forecast less weight child birth are Mother’s weight (pounds) before pregnant, age of Mother, during first three months the number of physician meet, number of early premature labors. The results of this work have improved prediction accuracy when compared with other optimization techniques. Maximum accuracy of 0.9629 is produced in proposed method.