Browsing by Author "Ngwenya, Mzabalazo"

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    Open Access
    A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data
    (2021) Mbaka, Sarah Kerubo; Ngwenya, Mzabalazo
    A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosis, patient-specific decisions or help in planning the utilization of resources for patient groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling and inference process itself. The challenges from the clinical domain include insufficient knowledge of temporal interactions of processes in the medical literature, the sparse nature and variability of medical data collection, and the difficulty in preparing and abstracting clinical data in a suitable format without losing valuable information in the process. Challenges about the DBN methodology and implementation include the lack of tools that allow easy modelling of temporal processes. Overcoming this challenge will help to solve various clinical temporal reasoning problems. In this thesis, we addressed these challenges while building a temporal network with explanations of the effects of predisposing factors, such as age and gender, and the progression information of all diagnoses using claims data from an insurance company in Kenya. We showed that our network could differentiate the possible probability exposure to a diagnosis given the age and gender and possible paths given a patient's history. We also presented evidence that the more patient history is provided, the better the prediction of future diagnosis.
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    Open Access
    An unsupervised approach to COVID-19 fake tweet detection
    (2024) Jarana, Bulungisa; Ngwenya, Mzabalazo
    Context: With the ongoing COVID-19 pandemic, social media platforms have become a crucial source of information. However, not all information shared on these platforms is accurate. The dissemination of fake news, intentional or unintentional, can lead to panic among readers and further exacerbate the effects of the pandemic. Objectives: This research project aims to explore the potential of unsupervised machine learning algorithms in differentiating between genuine and fake COVID-19 news shared on Twitter. The methodology includes a literature review, experimental analysis, and the utilization of a Twitter dataset. Methods: The study used both Mini-Batch K-means and K-means algorithms of clustering techniques to provide us with ‘grouping' of Twitter data in the two of clusters. Word embedding techniques such as TF-IDF, Word2Vec, and BERT were employed because machine learning models cannot process unprocessed text data directly, and word embedding resolves this issue. Results: The results on the test data show that K-means algorithm was the best performing algorithm (76% accuracy was achieved) in determining fake tweets about Covid-19. K-means algorithm using Bert word embedding is the best performing model followed by Mini-Batch K-means using TF-IDF word embedding (69% accuracy was achieved). Conclusions: The study demonstrates that clustering Twitter COVID-19 news as genuine or fake using K-means and Mini-Batch K-means algorithms is feasible Keywords: Clustering, Machine Learning, unsupervised learning, K-Means, MiniBatch K-Means, TF-IDF, Word2Vec, Bert, Confusion Matrix, Truncated SVD (Singular Value Decomposition), t-distributed stochastic neighbourhood embedding (t-SNE)
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    Open Access
    Anomaly detection in a mobile data network
    (2019) Salzwedel, Jason Paul; Ngwenya, Mzabalazo
    The dissertation investigated the creation of an anomaly detection approach to identify anomalies in the SGW elements of a LTE network. Unsupervised techniques were compared and used to identify and remove anomalies in the training data set. This “cleaned” data set was then used to train an autoencoder in an semi-supervised approach. The resultant autoencoder was able to indentify normal observations. A subsequent data set was then analysed by the autoencoder. The resultant reconstruction errors were then compared to the ground truth events to investigate the effectiveness of the autoencoder’s anomaly detection capability.