SU+ Digital Repository

SU+ is an online repository for the preservation and promotion of assorted digital content at Strathmore University

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Recent Submissions

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    Building a resilient sustainable economy through green and closed-loop supply chain management in the context of circular economy: a Sub-Saharan African manufacturing setting
    (Strathmore University, 2025) Aming'a, M. M.
    Over the past two to three decades, there has been a growing focus among scholars, practitioners, and policymakers on incorporating green, closed-loop, circular economy, sustainability, and resilience principles into managing supply chains. The increasing recognition of these principles as crucial elements in establishing stable and dependable supply chains has been spurred by various challenges in the global ecosystem, notably climate change. This study aimed to explore the development of a resilient and sustainable economy by employing green and closed-loop supply chain management within the framework of the circular economic model, specifically in the context of manufacturing businesses in a Sub-Saharan African economy. To achieve this objective, the study delved into integrating concepts that have received limited joint exploration in existing literature. In light of this context, a robust triadic conceptual framework was developed and subjected to empirical examination. The framework encompassed 83 factors organized into three aspects: (1) Practices associated with green and closed-loop supply chain management, (2) dimensions concerning resilient sustainability, and (3) internal environmental management practices. The suggested conceptual framework was tested through a novel structured survey distributed to 159 manufacturing businesses in a Sub-Saharan African economy. Survey participants included supply chain line managers, managing directors, and chief executive officers selected for their expertise and experience, resulting in 100 valuable responses. The empirical data collected was tested using the Partial Least Squares Structural Equation Modelling ran through Smart PLS version 4. The findings of the empirical investigations showed that practices such as cleaner production/green manufacturing, a combination of green procurement and design for the environment, and the application of reverse logistics had the most significant impact on the resilient sustainability of supply chains. The triadic-dimensional conceptual framework put forth in this study and its fundamental motivation represent novel contributions to existing literature. Moreover, this research explored a unique link in the investigation of green and closed-loop supply chain practices on the resilience of supply chains, offering a new avenue for industry practitioners and scholars to consider the uptake of green and closed-loop supply chain practices not only for their sustainability but also for their resilience and on their combined resilience and sustainability. This study is one of a few conducted in a Sub-Saharan manufacturing context and country, contributing valuable perspectives to the broader sustainable supply chain management body of knowledge.
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    A Real-time employee attrition prediction and risk scoring system
    (Strathmore University, 2025) Kariuki, M. M.
    Human Resource (HR) analytics is increasingly being explored around the globe for its potential in addressing employee attrition. Globally, the rate of attrition has been estimated to be about 25% higher in comparison to the pre-pandemic era. The effects of employee attrition including the loss of valuable talent and incurring costs for recruitment and on-boarding of new talent has been felt by companies in different sectors globally. Previous studies have made considerable efforts in not only understanding the concept of employee attrition but also in its early detection. This study aims to advance previous research by moving beyond merely identifying an effective machine learning technique to implementing the model that enables the human resource team to understand and assess employee attrition risk in real-time. This study provides a focus on three specific objectives that utilize human resource analytics approaches to understand the concept of attrition. Firstly, the study aims to use statistical approaches to analyze and identify the factors influencing employee attrition. Secondly, it aims to evaluate the effectiveness of machine learning algorithms in predicting employee attrition. Ultimately, the development of a system that predicts employee attrition and generates risk scores in real-time using relevant HR data marks a pivotal milestone for this study. Generalized Linear Model with interaction terms is the statistical approach which was utilized to assess the contributors of employee attrition. Job satisfaction, job involvement, years at company and monthly income were statistically significant thus are attributed to an employee’s decision to quit or stay. In this study, a performance evaluation and comparison of XGBoost, Random Forest (ensemble techniques) and Support Vector Machine, K- Nearest Neighbors as well as LogisticRegression machine learning models was conducted. Leveraging the employee records from the IBM dataset, Random Forest outperformed all the other models with an Accuracy of 80%, Precision of 91%, Recall of 85% and F1 Score at 88%. Insights from the first two research objectives were used to develop a real-time employee attrition and risk scoring tool. The solution provided under this study can be utilized in companies to provide data driven insights on attrition of their employee base. This study provides invaluable insights that can be used by various stakeholders including but not limited to, companies, data solution providers and the government to provide proactive measures to address attrition such as salary adjustment and management of employee work involvement. In conclusion, this study has contributed to the various on-going human resource analytic research which can be incorporated within organizational systems to address employee attrition and reduce costs incurred in recruitment and training of new talent. Keywords: Attrition, Human resource, Machine learning, Risk Scoring.
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    Improving maternal and neonatal health outcomes in Kenya by leveraging machine learning for the timely detection of preeclampsia
    (Strathmore University, 2025) Ngaruiya, E.
    Preeclampsia remains a leading cause of maternal and neonatal morbidity and mortality globally, with its burden disproportionately high in low-resource settings such as Kenya. Timely diagnosis is critical to improving outcomes, yet healthcare systems often lack the tools for early and reliable risk assessment. This study aimed to develop an AI-powered machine learning (ML) model to predict preeclampsia among pregnant women in Kenya, based on routinely collected clinical and demographic data. The specific objectives were: (a) to identify key risk factors using statistical analysis and clinical insight; (b) to develop and evaluate various ML models for preeclampsia prediction; (c) to determine the most accurate model for risk classification; and (d) to deploy the predictive system across multiple platforms for practical use. Using a dataset of 2,925 records collected from hospitals in Kenya's coastal region, the study applied extensive preprocessing to ensure data quality and clinical relevance. Variables included age, pre-pregnancy weight, systolic and diastolic blood pressure, proteinuria levels, parity, and history of hypertensive disorders. Five individual ML models—Logistic Regression, Random Forest, XGBoost, Support Vector Machine (SVM), and Decision Tree—were trained and evaluated. The XGBoost model emerged as the best individual performer, achieving an accuracy of 98.46%, precision of 95.59%, recall of 97.74%, and F1-score of 96.65%. However, a hybrid stacking classifier, which combined the predictions of multiple base models with Logistic Regression as a meta-learner, outperformed all individual models. It achieved an accuracy of 98.12%, a recall of 99.25%, and an F1-score of 96.00%, making it the most reliable for clinical deployment. The final model was deployed as a functional web-based application, allowing healthcare providers to input patient data and receive immediate risk assessments. This implementation underscores the potential of AI in enhancing prenatal care by enabling early intervention. The study concludes that ensemble-based ML models, especially stacking classifiers, provide a robust and scalable solution for preeclampsia risk prediction. Future work should focus on expanding the dataset to include diverse populations, integrating additional biomarkers, and developing mobile and EHR-compatible interfaces for broader reach in underserved areas.
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    Leveraging big data solutions and APRIORI algorithms for environmental sustainability in port cities a case study of Mombasa
    (Strathmore University, 2025) Chann, I.
    Big data has been dubbed "the next frontier for innovation, competition, and productivity," and is broadly described as "society's ability to harness information in creative ways to produce meaningful insights or commodities and services of significant value". The main research question for this study is: What are the potential contributions of spatial association mining based on the APRIORI algorithm for improving environmental sustainability in port cities in the global South? The study will employ a mixed-methods approach, which will include both qualitative and quantitative data collection and analysis techniques. Qualitative data will be collected through in-depth interviews with relevant stakeholders, such as government agencies, port authorities, and environmental organizations. Quantitative data will be collected through the analysis of satellite imagery and existing databases of environmental and shipping data. The APRIORI algorithm will be used to identify the significant associations between different environmental sustainability variables in the port cities. The expected findings from this research will provide evidence for the potential of spatial association mining based on the APRIORI algorithm for addressing environmental sustainability issues in port cities in the global South. The findings will also inform the development of more effective and efficient environmental sustainability policies and practices for these cities. This research will contribute to the field of environmental sustainability by demonstrating the potential of Big Data solutions for addressing environmental challenges in the global South. The results will provide a basis for further research on the use of data-driven approaches for environmental sustainability in other regions and sectors.
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    Unlocking biomedical data for AI health research in Africa using GeneNetwork
    (Strathmore University, 2025) Kilyungi, B. M.
    Genetic data analysis is essential for understanding biological processes and diseases. GeneNetwork (GN), an open-source platform with over 20 years of genetic and phenotypic data, relies on a complex relational database. However, the data is currently difficult to access and manipulate due to its complex underlying structures, including around 80 cross-referenced Structured Query Language (SQL) tables and various file types. This dissertation aimed to address the limitations of the GeneNetwork2 SQL database in representing and querying graph-like biological data by transforming it into the Resource Description Framework (RDF). A self documenting Domain Specific Language (DSL) was developed using GNU Guile to automate the conversion of GN’s MariaDB SQL database into RDF triples. This involved defining ontologies, mapping SQL views to RDF, and storing the data in Virtuoso. The framework’s effectiveness was evaluated by comparing query performance and output quality between SQL and SPARQL. Results showed that RDF transformation significantly improved query efficiency and semantic richness. At a 99.9% confidence level, SPARQL queries exhibit statistically significant faster execution times than the equivalent SQL queries. Additionally, RDF’s structured representation enabled intuitive querying and better relationship discovery, as demonstrated in retrieving mouse species details and searching GeneRIF entries. In conclusion, transforming GN’s data into RDF made complex queries faster and enhanced its FAIR (Findable, Accessible, Interoperable, Reusable) properties, improving accessibility through semantic enrichment and interoperability with federated services for both human and machine agents. This transformation unlocks the full potential of the data, laying the groundwork for a more adaptable, AI-ready GN service and providing valuable insights for the broader application of RDF in biological and clinical data integration. KEYWORDS: Artificial Intelligence, Data Accessibility, Data Interpretation, GeneNetwork, Biological Data, Data Discovery, Resource Description Framework (RDF), Metadata