University of Pittsburgh

Thesis Defense: Bayesian Networks for Diagnosing Childhood Malaria in Malawi

Student
Date: 
Friday, April 10, 2020 - 3:30pm - 5:00pm

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 such as Malawi. In this study, we aim to utilize Bayesian networks to diagnose cases of childhood malaria in Malawi. We develop two Bayesian diagnostic models for classification of malaria using clinical signs and symptoms. The first model is created manually, while the other combines an Augmented Naïve Bayes approach with expert knowledge. 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 Bayesian models is further compared to traditional machine learning classifiers on the basis of accuracy, AUC, precision, 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 Bayesian model achieves an accuracy of 63.6% with an AUC of 0.58. The augmented naïve Bayes model considers associations between the variables and achieves an accuracy of 62.7%. The Bayesian models outperform the logistic regression and random forest models in the classification of the disease. Bayesian 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 Bayesian models offers a unique approach to diagnostic support in low-resource countries. As Bayesian 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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