SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Sentiment Analysis of New Zealand Adults’ and Children’s Tweets Regarding the COVID-19 Vaccination Programme(Saudi Digital Library, 2023-12-02) Aldahmash, Lamyaa; Mpofu, CharlesThe SARS-CoV-2 virus, which caused the global COVID-19 pandemic, necessitated a significant worldwide response, with vaccination being a primary strategy. This dissertation explores the public sentiment towards New Zealand’s national vaccination campaign, through a machine learning analysis of large-scale text data gathered from the social media platform Twitter. Focusing on responses from both adults and children, this research aimed to assess the efficacy of health communication strategies and the wider acceptance of the vaccine within the community. The findings underscore a considerable disparity between policy decisions and public sentiment on Twitter, with a significant portion of the New Zealand population expressing negative views on vaccinations. Overall, this research reveals the need for enhanced public engagement, better communication, and more effective use of social media data by policymakers and healthcare professionals in order to address public concerns, mitigate fears, dispel misinformation, and ultimately increase vaccine uptake.5 0Item Restricted Pattern Recognition & Predictive Analysis of Cardiovascular Diseases: A Machine Learning Approach(Saudi Digital Library, 2023-11-23) Alseraihi, Faisal Fahad; Naich, AmmarCardiovascular disease (CVD) is a predominant global health concern, with its impact becoming increasingly pronounced in low- and middle- income countries due to challenges like limited healthcare access, inadequate public awareness, and lifestyle-related risks. Addressing CVD's multifactorial origins, which span genetic, environmental, and behavioral domains, requires advanced diagnostic techniques. This research leverages the UCI Heart Disease dataset to develop a deep learning predictive model for CVD, incorporating 14 vital heart health parameters. The models performance is critically assessed against conventional machine learning approaches, shedding light on its efficiency and areas of refinement. Utilizing sophisticated Neural Network structures, this study strives to enhance predictive health analytics, aiming for timely CVD identification and intervention. As the integration of machine learning into healthcare deepens, it's crucial to ensure that these tools are robust, thoroughly evaluated, and augment clinical insights to reduce misdiagnosis risks.69 0