School Of Natural Sciences

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    Development of a Health Insurance Premium Prediction Model using Machine Learning
    (Great Zimbabwe University, 2025) Makoni Tendai; Rukwava Caroline; Mawere Talent; Chinofunga Peter Tinashe
    In Zimbabwe’s evolving healthcare landscape, accurately determining health insurance premiums is critical to improving affordability, reducing risk imbalances, and increasing coverage, particularly amid economic constraints and rising health costs. Traditional actuarial models often struggle to represent the complex, non-linear relationships among socioeconomic, health, and lifestyle variables prevalent in the Zimbabwean population. This paper aims to develop a machine learning model that more precisely and rationally predicts health insurance premiums. Five supervised regression algorithms, Linear Regression (LR), LASSO Regression (LASSO), K-Nearest Neighbours (KNN), Random Forest (RF), and Gradient Boosting (GB), are evaluated for their effectiveness using a representative health insurance dataset that includes demographic and health-related attributes relevant to Zimbabwe. Models were assessed based on their Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results show that ensemble learning methods, particularly Gradient Boosting, significantly outperform traditional linear models, achieving the highest predictive accuracy. Key predictors of premium costs were identified as chronic illnesses, smoking status, and the number of dependents, variables that are particularly pertinent in local risk assessment. This paper advances health insurance analytics in Zimbabwe by providing evidence that machine learning can support more transparent, data-driven, and context-sensitive premium determination. The findings help insurers, policymakers, and healthcare stakeholders aiming to expand coverage and improve trust in private and public insurance schemes.
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    Introduction: Computational Intelligence and Mathematical Modelling for Industry and Commerce
    (Great Zimbabwe University, 2025) Nyawo Zvidenga Vongai
    g/0000-0003-2603-2273 Abstract - Between July 29 and July 31, 2025, GreatZimbabwe University’s School of Natural Sciences hosted its second International Conference on Computational Intelligence and Mathematics Modelling for Industry and Commerce, themed ‘Unlocking the Potential of the Fourth Industrial Revolution in Knowledge Work.’ The conference venue was the Victoria Falls Safari Lodge in Victoria Falls, Zimbabwe. The Minister of Higher and Tertiary Education, Honourable Dr Fredrick Shava, was the guest of Honour. The theme of the conference reflected the imperative to recalibrate the intellectual framework to respond to technological disruptions in the educational space. Great Zimbabwe University, as a Zimbabwean institution, is alert to the urgency of contextualizing the disruption within the national development agenda. The government of Zimbabwe, through flagship policies such as Heritage Based Education 5.0 and National Development Strategy 1, repositioned universities as engines of industrial transformation and national development. What this means is that institutions of higher learning go beyond teaching and learning, embrace innovation and industrialization. It is in this breath that the 2025 conference was relevant and apt. The fields of Computational Intelligence and Mathematical Modelling are emergent foundational academic disciplines to the implementation of Education 5.0.