http://hdl.handle.net/11189/1934 Wed, 12 Aug 2026 11:47:22 GMT 2026-08-12T11:47:22Z http://hdl.handle.net/11189/10638 Title: Performing record linkage and deduplication in master patient index using machine learning classifiers Authors: Hollenbach, Dane; Daramola, Olawande Abstract: The prevalence of duplicate patient records and the difficulties in accurately linking them is a big concern in healthcare. Duplicate records hinder quality healthcare, making the need for an effective Master Patient Index (MPI) system compelling. This paper investigated the performance of five machine learning classification algorithms (random forests, extreme gradient boosting, logistic regression, stacking ensemble, and deep multilayer perceptron) for data linkage and deduplication on four datasets. The result demonstrates the applicability of machine learning models for effective data linkage and deduplication of electronic health records. The random forest algorithm achieved the best performance (identifying duplicates correctly) based on accuracy, F1-Score, and AUC-score for three datasets (ePBRN: Acc = 99.83%, F1 = 81.09%, AUC = 99.98%; FEBRL3: Acc = 99.55%, F1 = 96.29%, AUC = 99.77%; Custom-synthetic: Acc = 99.98%, F1 = 99.18%, AUC = 99.99%). In contrast, the multilayer perceptron (Artificial Neural Network) had the best performance on the one dataset (FEBRL4: Acc = 99.93%, F1 = 96.95%, AUC = 99.97%). However, no single model was universally effective across all datasets, emphasising the need for tailored solutions. This study highlights the potential of machine learning to address the problem of duplicate records in healthcare specifically. By enhancing MPI systems, the study contributes to safer patient care and more efficient healthcare operations through improved record management. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11189/10638 2025-01-01T00:00:00Z http://hdl.handle.net/11189/10582 Title: Elicitation and prioritisation of requirements for a clinical decision support system for gait-related diseases in resource-limited settings Authors: Burger, Radford; Daramola, Olawande Abstract: Clinical Decision Support Systems (CDSS) have the potential to significantly improve healthcare quality in resource-limited settings (RLS). Despite evidence supporting the effectiveness of CDSS, their adoption and implementation rates remain low in RLS due to low levels of computer literacy among health workers, fragmented and unreliable infrastructure, and technical challenges. A thorough understanding of requirements is critical for the design of CDSS, which will be relevant to RLS. This paper explores the elicitation and prioritisation of requirements of a CDSS tailored to gait-related diseases in RLS. To do this, we conducted a qualitative literature analysis to identify potential requirements. After that, the requirements were presented to gait analysis experts for revision and prioritisation using the MoSCoW requirement prioritisation technique. The analysis of the results of the prioritisation process shows that for the functional requirements, 59.1% are fundamental and essential (Must Have), 36.3% are important but not fundamental (Should Have), 4.5% are negotiable requirements that are nice to have, but not important or fundamental (Could Have). All the non-functional requirements (100%) that pertain to usability and security were considered fundamental and essential (Must Have). This study provides a solid foundation for understanding the requirements of CDSS that are tailored to gait-related diseases in RLS. It also provides a guide for software developers and researchers on the design choices regarding the development of CDSS for RLS. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11189/10582 2025-01-01T00:00:00Z http://hdl.handle.net/11189/9982 Title: Strategy for knowledge transfer in AM as a hybrid process chain towards a transition from prototyping to commercialisation Authors: Makhetha, W.M.I. Abstract: Additive Manufacturing (AM) has gained considerable foot-print as one of the key components in making the 4th in-dustrial revolution a reality. Unlike traditional subtractive manufacturing processes which account for ~ 95% waste of material, AM provides almost unchallenged and sustainable manufacturing capabilities to drastically improve manufac-turing efficiency due to its nature of adding material as op-posed to removing it. Thereby, reducing life-cycle material mass and energy consumed. The ability to produce function-al 3D parts with customized and complex geometries direct-ly from CAD model data is particularly attractive. While metal AM processes such as laser powder bed fusion (L-PBF) are al-ready producing customized metal parts in applications such as dental implants, the full benefits of the technology have not been fully realized. This necessitates a global drive to learn best practices in AM towards new avenues for impact in teaching and learning, and in accelerated lab-to-market transition. The key to this is understanding inputs and out-puts of fundamental AM process parameters. This knowledge will help designers and potential end-users of the technolo-gy to quickly identify parameters which are most influential to structural integrity of parts produced. Considering that very little research has been performed on knowledge trans-fer among AM researchers, business and higher education, this paper is aimed at capacity building in AM technology by helping inexperienced users in higher education understand the technology better. Thereby, contributing to the inclusive global drive for an accelerated transition from prototyping to commercialization. The method used involves a stand-ard systematic triangulation of the literature to categorise and describe fundamental process parameters which influ-ence structural integrity of parts produced by the L-PBF. The findings of this work yield new knowledge in three domains. Firstly, the influential input parameters of L-PBF are identified as powder-specific, laser-specific and machine specific pa-rameters. Secondly, various post-processing solutions which are often used address the drawbacks associated with the technology are mapped out as thermodynamic, mechanical, and chemical post-processing treatments. Thirdly, the L-PBF is conceptualized into a framework which can help reshape the role of designers by identifying AM as a hybrid process and knowing what to look for when looking to make functional parts using technology. In this way, the paper contributes a novel skillset and attitude required to convert digital capabili-ties such as AM into valuable tools and methods. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/11189/9982 2023-01-01T00:00:00Z http://hdl.handle.net/11189/9968 Title: Determination of the end device risk likelihood using the Bayesian network tools Authors: Ncubukezi, Tabisa Abstract: All institutions use end devices for information processing which includes sending and receiving on the network. This process helps them to improve their business production as well as perform daily activities at a faster rate. However, the increased usage of end devices by both employees and criminals raises concerns and exposes businesses to a range of cyber risks. End devices can sometimes be used as agents and weapons to expose internal business operations. The vulnerability of the end devices to cyber threats and attacks compromises business data, its safety, and security. This paper determines the risk likelihood of the end devices using the Bayesian network tools. To achieve this, the study illustrates the connections of the end device variables to simulate the risk likelihood and its impact. The analysis and interpretation of the simulation are performed using decision tree analysis (DTA), scenario analysis, and sensitivity analysis techniques (Tornado graphs, conditional probability tables (CPT), and value of information configuration (VOI)). The relationship of the variables is demonstrated on the AgenaRisk package. Results revealed variables that influence the risk probability and its impact. End device risks can be caused by insiders and cyber criminals. The risks associated with end devices are influenced by the level of security implementation on different levels. The impact of the cyber risks was also accounted for and the concluding remarks were also made. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/11189/9968 2023-01-01T00:00:00Z