Conference Presentations by MARTIN MUDUVA
IEOM Society International, 2024
HIV and AIDS remain prominent global health concerns, and antiretroviral medication (ART) plays a... more HIV and AIDS remain prominent global health concerns, and antiretroviral medication (ART) plays a crucial role in treating infected individuals, preventing disease progression, and improving overall health outcomes. However, missed appointments in ART programs pose significant challenges by causing treatment interruptions, unsuppressed viral load, and increased HIV transmission rates. This research employs the CRISP-DM methodology and aims to develop a predictive model that effectively reduces missed appointments among people living with HIV. A comprehensive analysis of patient data, including demographics, clinical information, and appointment history, was conducted to determine the key factors influencing missed appointments. The prediction model was created using machine learning techniques such as decision trees, random forests, and support vector machines. It was determined that random forest produced the best results, having lower square errors and greater R squared. The findings contribute to the advancement of predictive analytics in healthcare, particularly in the context of chronic conditions such as HIV/AIDS.
IEOM Society International, 2024
In drought-prone African countries like Zimbabwe, the uptake of parametric insurance has been low... more 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 models-neural 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 wellinformed choices, adopt proactive measures, transfer the risk of drought-related losses, receive swift insurance payouts, and improve their financial resilience during drought events.
IEOM Society International, 2024
Diabetes is recognized as one of the world's most prevalent health problems. As diabetic patients... more Diabetes is recognized as one of the world's most prevalent health problems. As diabetic patients grew, so did the percentage of diabetic hospital readmissions. Early readmissions can impact patient well-being, operational efficiency, and financial burden. This study uses machine learning approaches to predict hospital readmissions among diabetes patients. Data was collected from 130 US hospitals. CRISP-DM is used for analysis. Logistic regression (LR) and random forest (RF) classifiers were implemented. The classifier performance was compared. Random Forest outperformed the other model, with an accuracy of 0.89. The model was chosen to enable practical deployments. Researchers used a web-based interface to get data and receive real-time predictions. The results showed that the predictive model used alongside an interface creates a clear and understandable prediction platform. However, the research might involve various datasets and Deep Learning to improve models and findings, in future studies. Furthermore, the model could explore the integration of machine learning interpretability approaches to increase transparency and promote better comprehension of the model's predictions by healthcare practitioners.
IEOM Society International, 2024
Mental health is an important aspect of well-being as it encompasses emotional, psychological and... more 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-Proceedings of the International Conference on Industrial Engineering and Operations Management ©IEOM Society International proof storage of information. Further research is needed for future advancements, like integrating blockchain-based patient portals with wearable devices and IoT.
Sciend and Information Conference - Springer, 2024
This research explains the perceptions of university students regarding the use of ChatGPT in the... more This research explains the perceptions of university students regarding the use of ChatGPT in their learning and research. The research is based on evidence provided in articles that were considered in a systematic literature review using the PRISMA model. The motivation for this study was informed by a realization that there are limited studies that provide a comprehensive understanding of students' perceptions regarding the use of ChatGPT in learning and research. Many existing studies either focus on educators' perceptions or isolated cases of empirical studies. It is therefore not clear what factors influence students' adoption of ChatGPT. This gap was addressed by this study by conducting a systematic literature review to identify factors influencing the adoption of ChatGPT, the positive impact of ChatGPT on students' education and the concerns raised by students regarding the integration of ChatGPT into academic practice. The research focus was on articles that discuss the students' perceptions of ChatGPT, all of which were drawn from Google Scholar. In total 32 articles were considered for analysis for this study and in this paper, we present five constructs that influence the adoption of ChatGPT in academic activities. Even though the students' perceptions of ChatGPT are widely varied, the findings of this research show that students concur that ChatGPT has a positive influence on their learning. The students further highlighted concerns relating to their use of ChatGPT in learning, which could be improved if the tool's educational effectiveness is to be realized. The contribution of this study is threefold. First, the findings of this research explain the perceptions of students about using ChatGPT in academic practice. Second, the results of this study inform the university management and policymakers about how to effectively integrate ChatGPT into the education system. Third, the study suggests, recommends, and guides educators who wish to incorporate this new, powerful tool (ChatGPT) into their teaching.
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Conference Presentations by MARTIN MUDUVA