Taneja, Sanya Bathla
(2020)
Bayesian Networks for Diagnosing Childhood Malaria in Malawi.
Master's Thesis, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Infectious diseases such as malaria are responsible for the majority of under-five deaths in low- and middle-income countries. Accurate diagnosis and management of illnesses can help in reducing the global burden of childhood morbidity and mortality. While trained healthcare workers deliver treatment for common childhood illnesses in healthcare facilities in Malawi, there is a significant lack of diagnostic support in rural health centers. With recent trends in artificial intelligence in global health, we hypothesize that a data-driven approach to diagnosis of childhood illnesses may address the challenges faced in health centers in low-resource countries. In this study, we aim to utilize Bayesian networks to diagnose cases of childhood malaria in Malawi. We develop two Bayesian network (BN) models for diagnosis of malaria using clinical signs and symptoms. The first model is created manually, while the other combines an Augmented Naïve Bayes approach with model editing by an expert. The models are learnt using a national survey dataset which contains sick child observations including patient information, diagnosis, and symptoms. The target malaria diagnosis is taken as the result of the malaria rapid diagnostic test (mRDT). The performance of the BN models is further compared to traditional machine learning classifiers on the basis of accuracy, area under the receiver operating characteristic curve (AUC), F1 score, sensitivity and specificity. We also present an experimental framework that can be used to model the malaria diagnostic support in the rural health centers. The manually created BN model achieves accuracy of 63.6% and AUC of 0.583. The Augmented Naïve Bayes model considers associations between the variables and achieves an accuracy of 62.7% and AUC of 0.581. BN models provide a powerful, efficient and data-driven tool for diagnosis of childhood illness that can lead to a more evidence-based clinical practice in Malawi. The simplicity and interpretability of BN models offers a unique approach to diagnostic support in low-resource countries. As BN models are representative of the population from which the data has been derived, this approach can be generalized to other childhood illnesses in different regions of the world.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
5 June 2020 |
| Date Type: |
Publication |
| Defense Date: |
10 April 2020 |
| Approval Date: |
5 June 2020 |
| Submission Date: |
20 April 2020 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
35 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Computing and Information > Intelligent Systems Program |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Bayesian Network, Malaria, Decision Analysis, Malawi |
| Date Deposited: |
05 Jun 2020 21:21 |
| Last Modified: |
05 Jun 2022 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/38993 |
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