Tezira Wanyana

Student Type: PhD

Tezira is Pursuing her PhD in the Adaptive and Cognitive Systems Lab at the University of Cape Town. Her research is on Designing and Evaluating an Agent-based Architecture for Automatic Knowledge Discovery and Evolution. She is also an Assistant Lecturer at Mbarara University of Science and Technology in the Department of Computer Science, Faculty of Computing and Informatics.

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

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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},
}

2021

Wanyana, T. . ., & Moodley, D. . (2021). An Agent Architecture for Knowledge Discovery and Evolution. In KI 2021: Advances in Artificial Intelligence (volume 12873). Cham: Springer International Publishing. http://doi.org/https://doi.org/10.1007/978-3-030-87626-5_18

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Wanyana-Moodley2021_Chapter_AnAgentArchitectureForKnowledg.pdf

The abductive theory of method (ATOM) was recently proposed to describe the process that scientists use for knowledge discovery. In this paper we propose an agent architecture for knowledge discovery and evolution (KDE) based on ATOM. The agent incorporates a combination of ontologies, rules and Bayesian networks for representing different aspects of its internal knowledge. The agent uses an external AI service to detect unexpected situations from incoming observations. It then uses rules to analyse the current situation and a Bayesian network for finding plausible explanations for unexpected situations. The architecture is evaluated and analysed on a use case application for monitoring daily household electricity consumption patterns.

@inbook{425,
  author = {Tezira Wanyana and Deshen Moodley},
  title = {An Agent Architecture for Knowledge Discovery and Evolution},
  abstract = {The abductive theory of method (ATOM) was recently proposed to describe the process that scientists use for knowledge discovery. In this paper we propose an agent architecture for knowledge discovery and evolution (KDE) based on ATOM. The agent incorporates a combination of ontologies, rules and Bayesian networks for representing different aspects of its internal knowledge. The agent uses an external AI service to detect unexpected situations from incoming observations. It then uses rules to analyse the current situation and a Bayesian network for finding plausible explanations for unexpected situations. The architecture is evaluated and analysed on a use case application for monitoring daily household electricity consumption patterns.},
  year = {2021},
  journal = {KI 2021: Advances in Artificial Intelligence},
  edition = {volume 12873},
  pages = {241-256},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-030-87626-5},
  doi = {https://doi.org/10.1007/978-3-030-87626-5_18},
}

2020

Wanyana, T. . ., Moodley, D. ., & Meyer, T. . (2020). An Ontology for Supporting Knowledge Discovery and Evolution. In First Southern African Conference for Artificial Intelligence Research. Virtual: SACAIR2020. Retrieved from https://2020.sacair.org.za/wp-content/uploads/2021/02/SACAIR_Proceedings-MainBook_Finv4_compressed.pdf?_ga=2.116601743.849395099.1621802506-572599210.1621419278

Abstract BibTex Entry BibTeX Download PDF

An Ontology for Supporting Knowledge Discovery and Evolution.pdf

Knowledge Discovery and Evolution (KDE) is of interest to a broad array of researchers from both Philosophy of Science (PoS) and Artificial Intelligence (AI), in particular, Knowledge Representation and Reasoning (KR), Machine Learning and Data Mining (ML-DM) and the Agent Based Systems (ABS) communities. In PoS, Haig recently pro- posed a so-called broad theory of scientific method that uses abduction for generating theories to explain phenomena. He refers to this method of scientific inquiry as the Abductive Theory of Method (ATOM). In this paper, we analyse ATOM, align it with KR and ML-DM perspectives and propose an algorithm and an ontology for supporting agent based knowledge discovery and evolution based on ATOM. We illustrate the use of the algorithm and the ontology on a use case application for electricity consumption behaviour in residential households.

@{405,
  author = {Tezira Wanyana and Deshen Moodley and Tommie Meyer},
  title = {An Ontology for Supporting Knowledge Discovery and Evolution},
  abstract = {Knowledge Discovery and Evolution (KDE) is of interest to a broad array of researchers from both Philosophy of Science (PoS) and Artificial Intelligence (AI), in particular, Knowledge Representation and Reasoning (KR), Machine Learning and Data Mining (ML-DM) and the Agent Based Systems (ABS) communities. In PoS, Haig recently pro- posed a so-called broad theory of scientific method that uses abduction for generating theories to explain phenomena. He refers to this method of scientific inquiry as the Abductive Theory of Method (ATOM). In this paper, we analyse ATOM, align it with KR and ML-DM perspectives and propose an algorithm and an ontology for supporting agent based knowledge discovery and evolution based on ATOM. We illustrate the use of the algorithm and the ontology on a use case application for electricity consumption behaviour in residential households.},
  year = {2020},
  journal = {First Southern African Conference for Artificial Intelligence Research},
  pages = {206-221},
  month = {22/02/2021},
  publisher = {SACAIR2020},
  address = {Virtual},
  isbn = {978-0-620-89373-2},
  url = {https://2020.sacair.org.za/wp-content/uploads/2021/02/SACAIR_Proceedings-MainBook_Finv4_compressed.pdf?_ga=2.116601743.849395099.1621802506-572599210.1621419278},
}