Browsing by Author "Muduva, M."

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    A Blockchain-based Patient Portal for Mental Health Management
    (IEOM Society International, USA, 2024-04-23) Jhamba, P.; Ndlovu, B.; Dube, S.; Muduva, M.; Jacqueline, F.; Maguraushe, K.
    Mental health is an important aspect of well-being as it encompasses emotional, psychological and social well-being. The use of patient portals in mental health care has gained attention as a potential tool to improve access to care for individuals with mental illness. Patient portals may be vulnerable to unauthorized access if appropriate security measures are not put in place. This study leverages blockchain technology to create tamper-proof patient records. The proposed solution uses an on-chain database that stores hashes and the actual medical record of a patient as well as an off-chain solution that handles encryption of each user’s medical record using their respective keys in a trustless manner before they are uploaded on-chain. A secure smart contract hosted on Ethereum and the Byzantine Fault Tolerance consensus algorithm was used to ensure patient privacy. The research employed the Comparative Analysis Research Methodology as the research methodology and the Kanban methodology as the software development methodology. The research project concludes that the proposed solution addresses the current security issues and data privacy concerns in patient data. The decentralized nature of blockchain ensures security, transparency, and tamper-proof storage of information. Further research is needed for future advancements, like integrating blockchain-based patient portals with wearable devices and IoT.
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    AI-based Drought Forecasting for Parametric Insurance
    (IEOM Society International, USA, 2024-05-07) Mathende, M.T.; Ndlovu, B.; Dube, S.; Muduva, M.; Kiwa, F.J.
    In drought-prone African countries like Zimbabwe, the uptake of parametric insurance has been low due to the absence of localized models. Guided by the CRISP-DM model, the present study proposes an AI-based approach to drought prediction in parametric insurance. The study’s paramount objectives are establishing trigger thresholds for drought events, assessing their significance, identifying the most effective machine learning models for drought modeling based on the Standardized Precipitation Index (SPI), and forecasting future drought occurrences and their magnitudes. Historical weather data, including temperature and rainfall, are utilized and a range of machine learning modelsneural networks, random forest, and support vector machines are employed for drought prediction. The performance of these models is evaluated based on accuracy, reliability, and interpretability, with continuous refinement based on feedback from stakeholders. The significance of this research lies in promoting data-driven decisions, incentivizing preparedness, enabling risk transfer, facilitating rapid insurance payouts, and enhancing financial stability. With accurate drought predictions driving parametric insurance, policyholders can make well-informed choices, adopt proactive measures, transfer the risk of drought-related losses, receive swift insurance payouts, and improve their financial resilience during drought events.