Classification infant mortality rates (IMR) using logistic regression ensemble (LORENS)

Munawar, Muhammad S., Karisma, Ria Dhea Layla Nur ORCID: https://orcid.org/0000-0002-5941-9565 and Kharisma, Dwi I. (2025) Classification infant mortality rates (IMR) using logistic regression ensemble (LORENS). Presented at The International Symposium on Biomathematics (SYMOMATH) 2023, 6–8 August 2023, Malang.

Preview

Text 23572.pdf - Published Version Download (842kB) | Preview

Abstract

Infant Mortality Rate (IMR) is the number of cases of infant mortality less than one-year-old divided by 1000 births. IMR is one of the most important indicators in determining public health problems. The number of IMR cases is influenced by several factors, such as healthcare services, the mother’s age at childbirth, and many more. Logistic Regression Ensemble (LORENS) is a classification method using ensemble techniques developed based on the Logistic Regression (LR) method. The advantage of LORENS is freedom from data dimension assumptions and the determination of classification classes using an optimal threshold. The method used to evaluate the goodness of the LORENS model is cross-validation. The purpose of this study is to obtain LR models and accuracy in the classification of infant mortality rates in Indonesia using LORENS. The best accuracy is achieved based on a training data to testing data ratio of 85%: 15% with two partitions and ten ensembles. The classification results of IMR using LORENS resulted in 20 LR models, with the breastfeeding variable being the most influential variable on infant mortality. The classification accuracy results are shown in the calculation of LORENS, which is 79.47%. The accuracy level of the LORENS model against IMR has a good value. Since, the cross-validation using LORENS has accuracy of the LORENS method using cross-validation, it showed an accuracy value of 78.87%.

Item Type:	Conference (Paper)
Keywords:	Health care; Public and occupational health and safety; Regression analysis
Subjects:	01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010402 Biostatistics
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Miss Ria Dhea Layla Nur Karisma
Date Deposited:	02 May 2025 09:46

Downloads

Origin of downloads

Actions (login required)

View Item