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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | Tropical Diseases, Travel Medicine and Vaccines |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40794-023-00208-7 |
| _version_ | 1797388557004832768 |
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| 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 |
| collection | DOAJ |
| 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 to 3.39, p = 0.333), and vomiting (AOR = 2.32; 95% CI: 0.85 to 6.82, p = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI: 0.41 to 0.90, p = 0.012) and BMI (AOR = 0.47, 95% CI: 0.26 to 0.80, p = 0.006). Conclusion Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict Pf antigen positivity may be a valuable tool for clinical decision-making. |
| first_indexed | 2024-03-08T22:41:36Z |
| format | Article |
| id | doaj.art-9d4dfe7223514396b16a82de74218908 |
| institution | Directory Open Access Journal |
| issn | 2055-0936 |
| language | English |
| last_indexed | 2024-03-08T22:41:36Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Tropical Diseases, Travel Medicine and Vaccines |
| spelling | doaj.art-9d4dfe7223514396b16a82de742189082023-12-17T12:07:26ZengBMCTropical Diseases, Travel Medicine and Vaccines2055-09362023-12-019111210.1186/s40794-023-00208-7Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatmentTaiwo Adetola Ojurongbe0Habeeb Abiodun Afolabi1Kehinde Adekunle Bashiru2Waidi Folorunso Sule3Sunday Babatunde Akinde4Olusola Ojurongbe5Nurudeen A. Adegoke6Department of Statistics, Osun State UniversityDepartment of Statistics, Osun State UniversityDepartment of Statistics, Osun State UniversityDepartment of Microbiology, Osun State UniversityDepartment of Microbiology, Osun State UniversityDepartment of Medical Microbiology and Parasitology, Ladoke Akintola University of TechnologyMelanoma Institute Australia, The University of SydneyAbstract 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 to 3.39, p = 0.333), and vomiting (AOR = 2.32; 95% CI: 0.85 to 6.82, p = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI: 0.41 to 0.90, p = 0.012) and BMI (AOR = 0.47, 95% CI: 0.26 to 0.80, p = 0.006). Conclusion Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict Pf antigen positivity may be a valuable tool for clinical decision-making.https://doi.org/10.1186/s40794-023-00208-7Environmental featuresMalariaMachine learningPredictionSocial-demographical behaviourSymptoms |
| spellingShingle | 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 Tropical Diseases, Travel Medicine and Vaccines Environmental features Malaria Machine learning Prediction Social-demographical behaviour Symptoms |
| title | 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_short | 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 |
| topic | Environmental features Malaria Machine learning Prediction Social-demographical behaviour Symptoms |
| url | https://doi.org/10.1186/s40794-023-00208-7 |
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