A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana.
<h4>Background</h4>Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths and disabilities related to snake...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-12-01
|
| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | https://doi.org/10.1371/journal.pntd.0012736 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849762056868200448 |
|---|---|
| author | Eric Nyarko Edmund Fosu Agyemang Ebenezer Kwesi Ameho Louis Agyekum José María Gutiérrez Eduardo Alberto Fernandez |
| author_facet | Eric Nyarko Edmund Fosu Agyemang Ebenezer Kwesi Ameho Louis Agyekum José María Gutiérrez Eduardo Alberto Fernandez |
| author_sort | Eric Nyarko |
| collection | DOAJ |
| description | <h4>Background</h4>Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths and disabilities related to snakebite envenoming by 2030. However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. This study aimed to combine established methodologies with the latest tools in Artificial Intelligence to assess the barriers to effective snakebite treatment in Ghana.<h4>Method</h4>We used a MaxDiff statistical experiment design to collect data, and six supervised machine learning models were applied to predict responses whose performance showed an advantage over the other through 6921 data points partitioned using the hold-back validation method, with 70% training and 30% validation. The results were compared using key metrics: Akaike Information Criterion corrected, Bayesian Information Criterion, Root Average Squared Error, and Fit Time in milliseconds.<h4>Results</h4>Considering all the responses, none of the six machine learning algorithms proved superior, but the Generalized Regression Model (Ridge) performed consistently better among the candidate models. The model consistently predicted several key significant barriers to effective snakebite treatment, such as the high cost of antivenoms, increased use of unorthodox, harmful practices, lack of access to effective antivenoms in remote areas when needed, and resorting to unorthodox and harmful practices in addition to hospital treatment.<h4>Conclusion</h4>The combination of a MaxDiff statistical experiment design to collect data and six machine learning models allowed the identification of barriers to accessing effective therapies for snakebite envenoming in Ghana. Addressing these barriers through targeted policy interventions, including intensified advocacy, continuous education, community engagement, healthcare worker training, and strategic investments, can enhance the effectiveness of snakebite treatment, ultimately benefiting snakebite victims and reducing the burden of snakebite envenoming. There is a need for robust regulatory frameworks and increased antivenom production to address these barriers. |
| format | Article |
| id | doaj-art-cee66163f7a343bc94edc8c2e0e7532b |
| institution | DOAJ |
| issn | 1935-2727 1935-2735 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj-art-cee66163f7a343bc94edc8c2e0e7532b2025-08-20T03:05:50ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352024-12-011812e001273610.1371/journal.pntd.0012736A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana.Eric NyarkoEdmund Fosu AgyemangEbenezer Kwesi AmehoLouis AgyekumJosé María GutiérrezEduardo Alberto Fernandez<h4>Background</h4>Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths and disabilities related to snakebite envenoming by 2030. However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. This study aimed to combine established methodologies with the latest tools in Artificial Intelligence to assess the barriers to effective snakebite treatment in Ghana.<h4>Method</h4>We used a MaxDiff statistical experiment design to collect data, and six supervised machine learning models were applied to predict responses whose performance showed an advantage over the other through 6921 data points partitioned using the hold-back validation method, with 70% training and 30% validation. The results were compared using key metrics: Akaike Information Criterion corrected, Bayesian Information Criterion, Root Average Squared Error, and Fit Time in milliseconds.<h4>Results</h4>Considering all the responses, none of the six machine learning algorithms proved superior, but the Generalized Regression Model (Ridge) performed consistently better among the candidate models. The model consistently predicted several key significant barriers to effective snakebite treatment, such as the high cost of antivenoms, increased use of unorthodox, harmful practices, lack of access to effective antivenoms in remote areas when needed, and resorting to unorthodox and harmful practices in addition to hospital treatment.<h4>Conclusion</h4>The combination of a MaxDiff statistical experiment design to collect data and six machine learning models allowed the identification of barriers to accessing effective therapies for snakebite envenoming in Ghana. Addressing these barriers through targeted policy interventions, including intensified advocacy, continuous education, community engagement, healthcare worker training, and strategic investments, can enhance the effectiveness of snakebite treatment, ultimately benefiting snakebite victims and reducing the burden of snakebite envenoming. There is a need for robust regulatory frameworks and increased antivenom production to address these barriers.https://doi.org/10.1371/journal.pntd.0012736 |
| spellingShingle | Eric Nyarko Edmund Fosu Agyemang Ebenezer Kwesi Ameho Louis Agyekum José María Gutiérrez Eduardo Alberto Fernandez A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. PLoS Neglected Tropical Diseases |
| title | A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. |
| title_full | A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. |
| title_fullStr | A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. |
| title_full_unstemmed | A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. |
| title_short | A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. |
| title_sort | supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in ghana |
| url | https://doi.org/10.1371/journal.pntd.0012736 |
| work_keys_str_mv | AT ericnyarko asupervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT edmundfosuagyemang asupervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT ebenezerkwesiameho asupervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT louisagyekum asupervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT josemariagutierrez asupervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT eduardoalbertofernandez asupervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT ericnyarko supervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT edmundfosuagyemang supervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT ebenezerkwesiameho supervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT louisagyekum supervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT josemariagutierrez supervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana AT eduardoalbertofernandez supervisedmachinelearningstatisticaldesignofexperimentapproachtomodelingthebarrierstoeffectivesnakebitetreatmentinghana |