Mbithe Nzomo

Student Type: PhD

My research explores the integration of data-based (e.g. machine learning) and knowledge-based (e.g. ontologies) AI in an agent architecture for health monitoring using sensors. I'm supervised by A/Prof. Deshen Moodley. Prior to this, I graduated with Distinction from the University of Manchester, where I earned an MSc in Advanced Computer Science with a specialization in AI.

Link: Website, LinkedIn

Publications

2022

Wanyana, T. . ., Nzomo, M. ., Price, C. S. ., & Moodley, D. . (2022). Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation. In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE. INSTICC: SciTePress. http://doi.org/https://doi.org/10.5220/0011046100003188

Abstract BibTex Entry BibTeX Download PDF

Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation.pdf

We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual

@inbook{478,
  author = {Tezira Wanyana and Mbithe Nzomo and C. Sue Price and Deshen Moodley},
  title = {Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation},
  abstract = {We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual},
  year = {2022},
  journal = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
  pages = {81-92},
  publisher = {SciTePress},
  address = {INSTICC},
  isbn = {978-989-758-566-1},
  doi = {https://doi.org/10.5220/0011046100003188},
}