Studying infant mortality: A demographic analysis based on data mining models
Child mortality, particularly among infants below 5 years, is a significant community well-being concern worldwide. The health sector’s top priority in emerging states is to minimize children’s death and enhance infant health. Despite a substantial decrease in worldwide deaths of children below 5 ye...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-07-01
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| Series: | Open Life Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/biol-2022-0643 |
| _version_ | 1797773514718052352 |
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| author | Satti Muhammad Islam Ali Mir Wajid Irshad Azeem Shah Mohd Asif |
| author_facet | Satti Muhammad Islam Ali Mir Wajid Irshad Azeem Shah Mohd Asif |
| author_sort | Satti Muhammad Islam |
| collection | DOAJ |
| description | Child mortality, particularly among infants below 5 years, is a significant community well-being concern worldwide. The health sector’s top priority in emerging states is to minimize children’s death and enhance infant health. Despite a substantial decrease in worldwide deaths of children below 5 years, it remains a significant community well-being concern. Children under five years of age died at 37 per 1,000 live birth globally in 2020. However, in underdeveloped countries such as Pakistan and Ethiopia, the fatality rate of children per 1,000 live birth is 65.2 and 48.7, respectively, making it challenging to reduce. Predictive analytics approaches have become well-known for predicting future trends based on previous data and extracting meaningful patterns and connections between parameters in the healthcare industry. As a result, the objective of this study was to use data mining techniques to categorize and highlight the important causes of infant death. Datasets from the Pakistan Demographic Health Survey and the Ethiopian Demographic Health Survey revealed key characteristics in terms of factors that influence child mortality. A total of 12,654 and 12,869 records from both datasets were examined using the Bayesian network, tree (J-48), rule induction (PART), random forest, and multi-level perceptron techniques. On both datasets, various techniques were evaluated with the aforementioned classifiers. The best average accuracy of 97.8% was achieved by the best model, which forecasts the frequency of child deaths. This model can therefore estimate the mortality rates of children under five years in Ethiopia and Pakistan. Therefore, an online model to forecast child death based on our research is urgently needed and will be a useful intervention in healthcare. |
| first_indexed | 2024-03-12T22:07:37Z |
| format | Article |
| id | doaj.art-f4c22a1d15964262ac50c574c3364e4a |
| institution | Directory Open Access Journal |
| issn | 2391-5412 |
| language | English |
| last_indexed | 2024-03-12T22:07:37Z |
| publishDate | 2023-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Life Sciences |
| spelling | doaj.art-f4c22a1d15964262ac50c574c3364e4a2023-07-24T11:18:21ZengDe GruyterOpen Life Sciences2391-54122023-07-0118119520610.1515/biol-2022-0643Studying infant mortality: A demographic analysis based on data mining modelsSatti Muhammad Islam0Ali Mir Wajid1Irshad Azeem2Shah Mohd Asif3Department of Computer Science, Millennium Institute of Technology & Entrepreneurship (MiTE), Karachi, PakistanDepartment of Computer Science, Millennium Institute of Technology & Entrepreneurship (MiTE), Karachi, PakistanFaculty of Computer Science, Asghar Mall College Rawalpindi, HED, Govt. of Punjab, PakistanKabridahar University, Kabridahar, EthiopiaChild mortality, particularly among infants below 5 years, is a significant community well-being concern worldwide. The health sector’s top priority in emerging states is to minimize children’s death and enhance infant health. Despite a substantial decrease in worldwide deaths of children below 5 years, it remains a significant community well-being concern. Children under five years of age died at 37 per 1,000 live birth globally in 2020. However, in underdeveloped countries such as Pakistan and Ethiopia, the fatality rate of children per 1,000 live birth is 65.2 and 48.7, respectively, making it challenging to reduce. Predictive analytics approaches have become well-known for predicting future trends based on previous data and extracting meaningful patterns and connections between parameters in the healthcare industry. As a result, the objective of this study was to use data mining techniques to categorize and highlight the important causes of infant death. Datasets from the Pakistan Demographic Health Survey and the Ethiopian Demographic Health Survey revealed key characteristics in terms of factors that influence child mortality. A total of 12,654 and 12,869 records from both datasets were examined using the Bayesian network, tree (J-48), rule induction (PART), random forest, and multi-level perceptron techniques. On both datasets, various techniques were evaluated with the aforementioned classifiers. The best average accuracy of 97.8% was achieved by the best model, which forecasts the frequency of child deaths. This model can therefore estimate the mortality rates of children under five years in Ethiopia and Pakistan. Therefore, an online model to forecast child death based on our research is urgently needed and will be a useful intervention in healthcare.https://doi.org/10.1515/biol-2022-0643data analyticsdemographic health surveyrule induction |
| spellingShingle | Satti Muhammad Islam Ali Mir Wajid Irshad Azeem Shah Mohd Asif Studying infant mortality: A demographic analysis based on data mining models Open Life Sciences data analytics demographic health survey rule induction |
| title | Studying infant mortality: A demographic analysis based on data mining models |
| title_full | Studying infant mortality: A demographic analysis based on data mining models |
| title_fullStr | Studying infant mortality: A demographic analysis based on data mining models |
| title_full_unstemmed | Studying infant mortality: A demographic analysis based on data mining models |
| title_short | Studying infant mortality: A demographic analysis based on data mining models |
| title_sort | studying infant mortality a demographic analysis based on data mining models |
| topic | data analytics demographic health survey rule induction |
| url | https://doi.org/10.1515/biol-2022-0643 |
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