Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support
The condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately. Despite this need, there are several gaps in research related to ma...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/17/2754 |
| _version_ | 1797582703233597440 |
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| author | Md. Shamshuzzoha Md. Motaharul Islam |
| author_facet | Md. Shamshuzzoha Md. Motaharul Islam |
| author_sort | Md. Shamshuzzoha |
| collection | DOAJ |
| description | The condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately. Despite this need, there are several gaps in research related to macrosomia, including limited predictive models, insufficient machine learning applications, ineffective interventions, and inadequate understanding of how to integrate machine learning models into clinical decision-making. To address these gaps, we developed a machine learning-based model that uses maternal characteristics and medical history to predict macrosomia. Three different algorithms, namely logistic regression, support vector machine, and random forest, were used to develop the model. Based on the evaluation metrics, the logistic regression algorithm provided the best results among the three. The logistic regression algorithm was chosen as the final algorithm to predict macrosomia. The hyper parameters of the logistic regression model were tuned using cross-validation to achieve the best possible performance. Our results indicate that machine learning-based models have the potential to improve macrosomia prediction and enable appropriate interventions for high-risk pregnancies, leading to better health outcomes for both mother and fetus. By leveraging machine learning algorithms and addressing research gaps related to macrosomia, we can potentially reduce the health risks associated with this condition and make informed decisions about high-risk pregnancies. |
| first_indexed | 2024-03-10T23:26:15Z |
| format | Article |
| id | doaj.art-ecb7f16440ad4488a560e41d5157bba2 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T23:26:15Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-ecb7f16440ad4488a560e41d5157bba22023-11-19T07:59:08ZengMDPI AGDiagnostics2075-44182023-08-011317275410.3390/diagnostics13172754Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision SupportMd. Shamshuzzoha0Md. Motaharul Islam1Department of CSE, United International University, Madani Avenue, Dhaka 1212, BangladeshDepartment of CSE, United International University, Madani Avenue, Dhaka 1212, BangladeshThe condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately. Despite this need, there are several gaps in research related to macrosomia, including limited predictive models, insufficient machine learning applications, ineffective interventions, and inadequate understanding of how to integrate machine learning models into clinical decision-making. To address these gaps, we developed a machine learning-based model that uses maternal characteristics and medical history to predict macrosomia. Three different algorithms, namely logistic regression, support vector machine, and random forest, were used to develop the model. Based on the evaluation metrics, the logistic regression algorithm provided the best results among the three. The logistic regression algorithm was chosen as the final algorithm to predict macrosomia. The hyper parameters of the logistic regression model were tuned using cross-validation to achieve the best possible performance. Our results indicate that machine learning-based models have the potential to improve macrosomia prediction and enable appropriate interventions for high-risk pregnancies, leading to better health outcomes for both mother and fetus. By leveraging machine learning algorithms and addressing research gaps related to macrosomia, we can potentially reduce the health risks associated with this condition and make informed decisions about high-risk pregnancies.https://www.mdpi.com/2075-4418/13/17/2754macrosomiamachine learningpredictive modelingobstetricspregnancyneonatal outcomes |
| spellingShingle | Md. Shamshuzzoha Md. Motaharul Islam Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support Diagnostics macrosomia machine learning predictive modeling obstetrics pregnancy neonatal outcomes |
| title | Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support |
| title_full | Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support |
| title_fullStr | Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support |
| title_full_unstemmed | Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support |
| title_short | Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support |
| title_sort | early prediction model of macrosomia using machine learning for clinical decision support |
| topic | macrosomia machine learning predictive modeling obstetrics pregnancy neonatal outcomes |
| url | https://www.mdpi.com/2075-4418/13/17/2754 |
| work_keys_str_mv | AT mdshamshuzzoha earlypredictionmodelofmacrosomiausingmachinelearningforclinicaldecisionsupport AT mdmotaharulislam earlypredictionmodelofmacrosomiausingmachinelearningforclinicaldecisionsupport |