Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression
Abstract Background Cardiovascular diseases (CVD) are major contributors to maternal mortality and morbidity during pregnancy and increased atherogenic index of plasma levels is associated with a higher risk of CVD and obesity. Methods In this study, we utilized three different machine learning algo...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Bulletin of the National Research Centre |
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| Online Access: | https://doi.org/10.1186/s42269-024-01295-y |
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| author | Idris Zubairu Sadiq Fatima Sadiq Abubakar Muhammad Auwal Saliu Babangida Sanusi katsayal Aliyu Salihu Aliyu Muhammad |
| author_facet | Idris Zubairu Sadiq Fatima Sadiq Abubakar Muhammad Auwal Saliu Babangida Sanusi katsayal Aliyu Salihu Aliyu Muhammad |
| author_sort | Idris Zubairu Sadiq |
| collection | DOAJ |
| description | Abstract Background Cardiovascular diseases (CVD) are major contributors to maternal mortality and morbidity during pregnancy and increased atherogenic index of plasma levels is associated with a higher risk of CVD and obesity. Methods In this study, we utilized three different machine learning algorithms (boosting, random forest, and decision tree regression) to predict dyslipidemia-associated cardiovascular disease using atherogenic index and lipid profile parameters based on a cross-sectional study datasets of 112 pregnant women aged between 15 and 49 conducted at Aminu Kano Teaching Hospital. Results The results showed that random forest regression outperformed both boosting and decision tree regression, recording the lowest error criteria (MSE = 0.071 and RMSE = 0.266) for evaluating the model. These findings indicated that all the three algorithms have the potential to effectively model the data from atherogenic indices and lipid profile parameters but random forest and boosting were found to outperform decision tree models with respective R2 values of 0.95 and 0.92. Conclusions Overall, the study highlights the accuracy of machine learning models (random forest, boosting, and decision trees) in predicting dyslipidemia-associated cardiovascular diseases and the findings could contribute to the development of effective strategies for the prevention and treatment of dyslipidemia-associated cardiovascular diseases. |
| format | Article |
| id | doaj-art-557d455c6e314ba496c123a37648122b |
| institution | Kabale University |
| issn | 2522-8307 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Bulletin of the National Research Centre |
| spelling | doaj-art-557d455c6e314ba496c123a37648122b2025-01-12T12:07:10ZengSpringerOpenBulletin of the National Research Centre2522-83072025-01-0149111310.1186/s42269-024-01295-yMachine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regressionIdris Zubairu Sadiq0Fatima Sadiq Abubakar1Muhammad Auwal Saliu2Babangida Sanusi katsayal3Aliyu Salihu4Aliyu Muhammad5Department of Biochemistry, Faculty of Life Sciences, Ahmadu Bello UniversityDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello UniversityDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello UniversityDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello UniversityDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello UniversityDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello UniversityAbstract Background Cardiovascular diseases (CVD) are major contributors to maternal mortality and morbidity during pregnancy and increased atherogenic index of plasma levels is associated with a higher risk of CVD and obesity. Methods In this study, we utilized three different machine learning algorithms (boosting, random forest, and decision tree regression) to predict dyslipidemia-associated cardiovascular disease using atherogenic index and lipid profile parameters based on a cross-sectional study datasets of 112 pregnant women aged between 15 and 49 conducted at Aminu Kano Teaching Hospital. Results The results showed that random forest regression outperformed both boosting and decision tree regression, recording the lowest error criteria (MSE = 0.071 and RMSE = 0.266) for evaluating the model. These findings indicated that all the three algorithms have the potential to effectively model the data from atherogenic indices and lipid profile parameters but random forest and boosting were found to outperform decision tree models with respective R2 values of 0.95 and 0.92. Conclusions Overall, the study highlights the accuracy of machine learning models (random forest, boosting, and decision trees) in predicting dyslipidemia-associated cardiovascular diseases and the findings could contribute to the development of effective strategies for the prevention and treatment of dyslipidemia-associated cardiovascular diseases.https://doi.org/10.1186/s42269-024-01295-yMachine learningAlgorithmsCardiovascular diseasesPregnancyAtherogenic index |
| spellingShingle | Idris Zubairu Sadiq Fatima Sadiq Abubakar Muhammad Auwal Saliu Babangida Sanusi katsayal Aliyu Salihu Aliyu Muhammad Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression Bulletin of the National Research Centre Machine learning Algorithms Cardiovascular diseases Pregnancy Atherogenic index |
| title | Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression |
| title_full | Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression |
| title_fullStr | Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression |
| title_full_unstemmed | Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression |
| title_short | Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression |
| title_sort | machine learning algorithms for predictive modeling of dyslipidemia associated cardiovascular disease risk in pregnancy a comparison of boosting random forest and decision tree regression |
| topic | Machine learning Algorithms Cardiovascular diseases Pregnancy Atherogenic index |
| url | https://doi.org/10.1186/s42269-024-01295-y |
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