University of Gondar Reports Findings in Machine Learning (Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods): Machine Learning

2023 JAN 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Machine Learning is the subject of a report. According to news reporting originating in Gondar, Ethiopia, by NewsRx journalists, research stated, “Perinatal mortality in Ethiopia is the highest in Africa, with 68 per 1000 pregnancies intrapartum deaths. It is mainly associated with home delivery, which contributes to more than 75% of perinatal deaths.”

The news reporters obtained a quote from the research from the University of Gondar, “Financial constraints significantly impact timely access to maternal health care. Financial incentives, such as health insurance, may address the demand- and supply-side factors. This study, hence, aims to predict perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods. The data was collected from the Ethiopian demographic health survey from 2011 to 2019 G.C. The data were pre-processed to get quality data that are suitable for the homogenous ensemble machine-learning algorithms to develop a model that predicts perinatal mortality. We have applied filter (chi-square and mutual information) and wrapper (sequential forward and sequential backward) feature selection methods. After selecting all the relevant features, we developed a predictive model using cat boost, random forest, and gradient boosting algorithms and evaluated the model using both objective (accuracy, precision, recall, F1_score, ROC) and subjective (domain expert) based evaluation techniques. Perinatal mortality prediction models were developed using random forest, gradient boosting, and cat boost algorithms with the overall accuracy of 89.95%, 90.24%, and 82%, respectively. Risk factors of perinatal mortality were identified using feature importance analysis and relevant rules were extracted using the best performing model. A prediction model that was developed using gradient boosting algorithms was selected for further use in the risk factor analysis, generating relevant rules, development of artifacts, and model deployment because it has registered better performance with 90.24% accuracy.”

According to the news reporters, the research concluded: “The most determinant risk factors of perinatal mortality were identified using feature importance and some of them are community-based health insurance, mother’s educational level, region and place of residence, age, wealth status, birth interval, preterm, smoking cigarette, anemia level, hemoglobin level, and marital status.”

For more information on this research see: Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods. BMC Medical Informatics and Decision Making, 2022;22(1):341. BMC Medical Informatics and Decision Making can be contacted at: Bmc, Campus, 4 Crinan St, London N1 9XW, England. (BioMed Central - http://www.biomedcentral.com/; BMC Medical Informatics and Decision Making - http://www.biomedcentral.com/bmcmedinformdecismak/)

Our news correspondents report that additional information may be obtained by contacting Dawit S. Bogale, College of Informatics, University of Gondar, Gondar, Ethiopia. Additional authors for this research include Tesfamariam M. Abuhay and Belayneh E. Dejene.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1186/s12911-022-02084-1. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

The publisher of the journal BMC Medical Informatics and Decision Making can be contacted at: Bmc, Campus, 4 Crinan St, London N1 9XW, England.

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