Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment
Abstract Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Con...
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Online Access: | https://doi.org/10.1186/s40794-023-00208-7 https://doaj.org/article/9d4dfe7223514396b16a82de74218908 |
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ftdoajarticles:oai:doaj.org/article:9d4dfe7223514396b16a82de74218908 2024-01-21T10:04:17+01:00 Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment Taiwo Adetola Ojurongbe Habeeb Abiodun Afolabi Kehinde Adekunle Bashiru Waidi Folorunso Sule Sunday Babatunde Akinde Olusola Ojurongbe Nurudeen A. Adegoke 2023-12-01T00:00:00Z https://doi.org/10.1186/s40794-023-00208-7 https://doaj.org/article/9d4dfe7223514396b16a82de74218908 EN eng BMC https://doi.org/10.1186/s40794-023-00208-7 https://doaj.org/toc/2055-0936 doi:10.1186/s40794-023-00208-7 2055-0936 https://doaj.org/article/9d4dfe7223514396b16a82de74218908 Tropical Diseases, Travel Medicine and Vaccines, Vol 9, Iss 1, Pp 1-12 (2023) Environmental features Malaria Machine learning Prediction Social-demographical behaviour Symptoms Arctic medicine. Tropical medicine RC955-962 article 2023 ftdoajarticles https://doi.org/10.1186/s40794-023-00208-7 2023-12-24T01:47:20Z Abstract Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Consequently, the addition of automated detection and classification of malaria using laboratory methods can provide patients with more accurate and faster diagnosis. Therefore, this study used a machine-learning model to predict Plasmodium falciparum (Pf) antigen positivity (presence of malaria) based on sociodemographic behaviour, environment, and clinical features. Method Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Outcomes were classified as Pf-positive or Pf-negative, corresponding to the presence or absence of malaria, respectively. Results Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic curve for the training set (AUC = 84%; 95% confidence interval [CI]: 75–93%) and test set (AUC = 83%; 95% CI: 63–100%). Increased odds of malaria were associated with higher body weight (adjusted odds ratio (AOR) = 4.50, 95% CI: 2.27 to 8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was not significant, patients with high body temperature had higher odds of testing positive for the Pf antigen than those who did not have high body temperature (AOR = 1.40, 95% CI: 0.99 to 1.91, p = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI: 1.30 to 4.66, p = 0.006) or experienced fever (AOR = 2.10, 95% CI: 0.88 to 4.24, p = 0.099), headache (AOR = 2.07; 95% CI: 0.95 to 3.95, p = 0.068), muscle pain (AOR = 1.49; 95% CI: 0.66 ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Tropical Diseases, Travel Medicine and Vaccines 9 1 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Environmental features Malaria Machine learning Prediction Social-demographical behaviour Symptoms Arctic medicine. Tropical medicine RC955-962 |
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Environmental features Malaria Machine learning Prediction Social-demographical behaviour Symptoms Arctic medicine. Tropical medicine RC955-962 Taiwo Adetola Ojurongbe Habeeb Abiodun Afolabi Kehinde Adekunle Bashiru Waidi Folorunso Sule Sunday Babatunde Akinde Olusola Ojurongbe Nurudeen A. Adegoke Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment |
topic_facet |
Environmental features Malaria Machine learning Prediction Social-demographical behaviour Symptoms Arctic medicine. Tropical medicine RC955-962 |
description |
Abstract Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Consequently, the addition of automated detection and classification of malaria using laboratory methods can provide patients with more accurate and faster diagnosis. Therefore, this study used a machine-learning model to predict Plasmodium falciparum (Pf) antigen positivity (presence of malaria) based on sociodemographic behaviour, environment, and clinical features. Method Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Outcomes were classified as Pf-positive or Pf-negative, corresponding to the presence or absence of malaria, respectively. Results Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic curve for the training set (AUC = 84%; 95% confidence interval [CI]: 75–93%) and test set (AUC = 83%; 95% CI: 63–100%). Increased odds of malaria were associated with higher body weight (adjusted odds ratio (AOR) = 4.50, 95% CI: 2.27 to 8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was not significant, patients with high body temperature had higher odds of testing positive for the Pf antigen than those who did not have high body temperature (AOR = 1.40, 95% CI: 0.99 to 1.91, p = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI: 1.30 to 4.66, p = 0.006) or experienced fever (AOR = 2.10, 95% CI: 0.88 to 4.24, p = 0.099), headache (AOR = 2.07; 95% CI: 0.95 to 3.95, p = 0.068), muscle pain (AOR = 1.49; 95% CI: 0.66 ... |
format |
Article in Journal/Newspaper |
author |
Taiwo Adetola Ojurongbe Habeeb Abiodun Afolabi Kehinde Adekunle Bashiru Waidi Folorunso Sule Sunday Babatunde Akinde Olusola Ojurongbe Nurudeen A. Adegoke |
author_facet |
Taiwo Adetola Ojurongbe Habeeb Abiodun Afolabi Kehinde Adekunle Bashiru Waidi Folorunso Sule Sunday Babatunde Akinde Olusola Ojurongbe Nurudeen A. Adegoke |
author_sort |
Taiwo Adetola Ojurongbe |
title |
Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment |
title_short |
Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment |
title_full |
Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment |
title_fullStr |
Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment |
title_full_unstemmed |
Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment |
title_sort |
prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment |
publisher |
BMC |
publishDate |
2023 |
url |
https://doi.org/10.1186/s40794-023-00208-7 https://doaj.org/article/9d4dfe7223514396b16a82de74218908 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Tropical Diseases, Travel Medicine and Vaccines, Vol 9, Iss 1, Pp 1-12 (2023) |
op_relation |
https://doi.org/10.1186/s40794-023-00208-7 https://doaj.org/toc/2055-0936 doi:10.1186/s40794-023-00208-7 2055-0936 https://doaj.org/article/9d4dfe7223514396b16a82de74218908 |
op_doi |
https://doi.org/10.1186/s40794-023-00208-7 |
container_title |
Tropical Diseases, Travel Medicine and Vaccines |
container_volume |
9 |
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1 |
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1788694679789764608 |