Machine learning model for predicting fetal nutritional status
Malnutrition tends to be one of the most important reasons for child mortality in Tanzania and other developing countries, in most cases during the first five years of life. This research was conducted todevelop machine learning model for predicting fetal nutritional status. Several machine learning...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
International Academy of Ecology and Environmental Sciences
2024-03-01
|
| Series: | Computational Ecology and Software |
| Subjects: | |
| Online Access: | http://www.iaees.org/publications/journals/ces/articles/2024-14(1)/machine-learning-predicting-fetal-nutritional-status.pdf |
| _version_ | 1827347958046130176 |
|---|---|
| author | B. Selemani D. Machuve N. Mduma |
| author_facet | B. Selemani D. Machuve N. Mduma |
| author_sort | B. Selemani |
| collection | DOAJ |
| description | Malnutrition tends to be one of the most important reasons for child mortality in Tanzania and other developing countries, in most cases during the first five years of life. This research was conducted todevelop machine learning model for predicting fetal nutritional status. Several machine learning techniques such as AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, Decision Tree, K-nearest neighbor and Stochastic Gradient Descent, were used to categorize the children in the test dataset as "malnourished" or "nourished". The accuracy, sensitivity, and specificity of these algorithms' prediction abilities were comparedusing performance measures such as accuracy, sensitivity, and specificity. Results show that malnutrition status can be predicted using Random Forest machine learning technique which was about 98% and brings positive impact to the society. The study findings indicated a need for more attention on nutrition to expected mothers and children under five to be well administered with the government and the society at large by putting relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for the future society. |
| first_indexed | 2024-03-08T00:02:48Z |
| format | Article |
| id | doaj.art-2fa6db80821c49b0a0fd8ef2dd4bb4b2 |
| institution | Directory Open Access Journal |
| issn | 2220-721X |
| language | English |
| last_indexed | 2024-03-08T00:02:48Z |
| publishDate | 2024-03-01 |
| publisher | International Academy of Ecology and Environmental Sciences |
| record_format | Article |
| series | Computational Ecology and Software |
| spelling | doaj.art-2fa6db80821c49b0a0fd8ef2dd4bb4b22024-02-18T01:39:30ZengInternational Academy of Ecology and Environmental SciencesComputational Ecology and Software2220-721X2024-03-011416876Machine learning model for predicting fetal nutritional statusB. Selemani0D. Machuve1N. Mduma2Nelson Mandela African Institution of Science and Technology, Arusha TanzaniaNelson Mandela African Institution of Science and Technology, Arusha TanzaniaNelson Mandela African Institution of Science and Technology, Arusha TanzaniaMalnutrition tends to be one of the most important reasons for child mortality in Tanzania and other developing countries, in most cases during the first five years of life. This research was conducted todevelop machine learning model for predicting fetal nutritional status. Several machine learning techniques such as AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, Decision Tree, K-nearest neighbor and Stochastic Gradient Descent, were used to categorize the children in the test dataset as "malnourished" or "nourished". The accuracy, sensitivity, and specificity of these algorithms' prediction abilities were comparedusing performance measures such as accuracy, sensitivity, and specificity. Results show that malnutrition status can be predicted using Random Forest machine learning technique which was about 98% and brings positive impact to the society. The study findings indicated a need for more attention on nutrition to expected mothers and children under five to be well administered with the government and the society at large by putting relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for the future society.http://www.iaees.org/publications/journals/ces/articles/2024-14(1)/machine-learning-predicting-fetal-nutritional-status.pdfmalnutritionmobile applicationmachine learningtanzania |
| spellingShingle | B. Selemani D. Machuve N. Mduma Machine learning model for predicting fetal nutritional status Computational Ecology and Software malnutrition mobile application machine learning tanzania |
| title | Machine learning model for predicting fetal nutritional status |
| title_full | Machine learning model for predicting fetal nutritional status |
| title_fullStr | Machine learning model for predicting fetal nutritional status |
| title_full_unstemmed | Machine learning model for predicting fetal nutritional status |
| title_short | Machine learning model for predicting fetal nutritional status |
| title_sort | machine learning model for predicting fetal nutritional status |
| topic | malnutrition mobile application machine learning tanzania |
| url | http://www.iaees.org/publications/journals/ces/articles/2024-14(1)/machine-learning-predicting-fetal-nutritional-status.pdf |
| work_keys_str_mv | AT bselemani machinelearningmodelforpredictingfetalnutritionalstatus AT dmachuve machinelearningmodelforpredictingfetalnutritionalstatus AT nmduma machinelearningmodelforpredictingfetalnutritionalstatus |