Predicting premature birth during pregnancy: A case study using decision trees, naive bayes, knn, and random forest

Muhammad Modi Lakulu

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Type :	Article
Subject :	T Technology (General)
ISBN :	1660-6795
Main Author :	Muhammad Modi Lakulu
Additional Authors :	Ismail @ Ismail Yusuf Panessai
Title :	Predicting premature birth during pregnancy: A case study using decision trees, naive bayes, knn, and random forest
Hits :	202

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran & Meta-Teknologi
Year of Publication :	2024
Notes :	Nanotechnology Perceptions
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
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Abstract : Universiti Pendidikan Sultan Idris

Premature birth remains a significant challenge in maternal health services, underscoring the need for effective predictive models to enable early detection and intervention. This study examined the performance of four machine learning algorithms: Decision Trees, Naive Bayes, KNN, and Random Forest in predicting preterm birth during pregnancy. Utilizing data collected from pregnant individuals, encompassing maternal health indicators and fetal development metrics, our model aimed to forecast the likelihood of preterm birth. We assessed the predictive ability of each model by evaluating metrics such as accuracy, precision, recall, sensitivity, specificity, and area under the curve (AUC). The results revealed variability in model performance, with Logistic Random Forest exhibiting strong performance. This suggests its potential utility in clinical settings for the early detection and intervention of preterm pregnancy. Our study contributes to advancements in predictive modelling within maternal health services, aiming to enhance maternal and fetal health outcomes through the early identification of preterm birth. _ 2024, Collegium Basilea. All rights reserved.

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