Theses (Master In Health Informatics & Analytics)

The accurate assessment of dietary intake is crucial for promoting health and managing diet-related conditions such as diabetes and obesity. Traditional methods of dietary assessment are often prone to inaccuracies and time-consuming. This study evaluates the effectiveness of three deep learning models U-Net with 16 filters, U-Net with 64 filters, and the Segment Anything Model (SAM) for segmenting carbohydrate regions in food images. The models were assessed using metrics such as accuracy, Intersection over Union (IoU), and Dice Score. The SAM model outperformed the U-Net models, achieving an overall accuracy of 99.24%, an IoU of 90.59%, and a Dice Score of 94.21%. The UNet 16-filter model showed better performance than the 64-filter model, with an accuracy of 97.86% and an IoU of 81.15%. These results highlight SAM's advanced capabilities in promptable segmentation and zero-shot transfer, making it the most effective model for this task. Future research should focus on expanding the dataset, integrating texture-based segmentation methods, and exploring data augmentation techniques to further enhance model robustness.

Introduction: Advancement in information technology in healthcare and data visualisation should be leveraged to address the challenge of high doctor turnover, ultimately improving healthcare services for the public. The Malaysian government is currently gappling with issues related to brain drain, as highlighted by the Prime Minister during the Malaysia Madani economic development event in September 2023. Methodology: This research has three primary objectives: (I) to identify the key factors influencing doctors' work experience in the public hospital, (2) to design a dashboard for monitoring these key factors, and (3) to validate the dashboard with associated experts. The study utilised 301 primary datasets from Hospital Kuala Lumpur and Hospital Tunku Azizah, employing a cross-sectional, convenience purposive sampling method. The focus is on sociodemographic, socioeconomic, work, psychosocial and job satisfaction factors. The study used a structured questionnaire consisting of 80 questions. Results: Six key factors were identified through simple logistic regression (P<0.05): age group, total length of service, job position, total income, job satisfaction and burnout. Among these, job position and job satisfaction were selected as predictors through the hierarchical multiple logistic regression. The customised dashboard, designed using Tableau version 2023.2.1, provides interactive and actionable insights. The dashboard was validated by respective experts to ensure accuracy, usability, and functionality. Conclusion: Identifying key factors and designing an interactive dashboard are crucial for understanding and improving with doctors' work experiences in public hospitals. These insights can guide government policy-making to help retain the doctors and enhance the national healthcare system. Keywords: Doctor, Work experience, Job satisfaction, burnout, dashboard

Dental implants success poses a significant challenge in oral rehabilitation, influenced by various patients-specific and surgical factors. The objective of this study is to employ supervised machine learning (ML) algorithms to predict dental implant failure emphasizing on feature interaction analysis and its impact on model accuracy and reliability. 747 implants record dataset was utilised consisting of features related to patient surgeon and surgery. Features were selected based on statistical test and backward elimination. Greenwell's method and Freidman's H statistical method were employed to identify interacting pairs, followed by modelling with Random Forest, K Nearest Neighbor and Extreme Gradient Boosting with hyper parameter tuning via Standardized Search CV and Grid Search CV. While some interacting pairs improved model accuracy marginally, not all interactions led to improvement. With 'ridge augmentation and age' interacting pair, XG Boost achieved the highest accuracy of 89.04%. However, it could not surpass the Random Forest accuracy of 90.4% achieved through feature selection and hyperparameter tuning without interactions. The study demonstrated the potential oi feature interaction modelling in predicting dental implant failure but emphasized the careful selection of interactions. Future research can refine the understanding of feature interactions and its impact on predictive models, leading to enhanced clinical decision making in dental implantology.

The world is moving fast towards an ageing population, with older adults representing a significant and growing proportion of the population. As older adults age, their health and well-being become increasingly important. The use of the Internet for health information gathering among older adults is a topic of growing interest in research and policy discussions and is a growing concern. Motivation for this study stemmed from the need to understand patterns and behaviours of how older adults use the internet for health information seeking. The use of the internet has been prevalent since the year 2000 and the older adults are not left behind in utilizing this resource. There has been a growing body of research on the use of technology, including the internet, by older adults. However, there is still limited research on the specific use of the internet for health information gathering among older adults. The aim of the study is to explore the potential benefits and ethical implications of Internet-based information gathering in promoting active ageing, with a focus on data analytics and visualization as an approach. The rationale for this study is to analyze the data collected on how the elderly use the Internet for health information seeking to add more insightful information to plan an effective policy to support the better use of the Internet for them in the future. By exploring this, policymakers can plan effective policies and support the elderly in better utilizing the internet for their health needs . The methodology for this study involved a quantitative and analytical approach to visualizing the data collected on the use of the internet for health information seeking among older adults. The data obtained from the National Health Institute (NIH), Ministry of Health Malaysia survey conducted in 2017 will serve as the baseline data on the use of the internet in searching for health information by the elderly. For this study, we will focus on the data analytics and visualization approach to exploring the potential benefits and ethical implications of internet-based information gathering for active ageing among the older adults. The findings of this study will provide valuable insights into the patterns and behaviours of older adults in using the internet for health information seeking. First of all, there are positive indications that the older adults that use the internet for health information seeking trusts its source and finds it useful. There is also evidence of active participation in social activities and also positive behaviours in taking actions upon getting health information from the internet like making lifestyle changes or seeking medical advice. The potential benefits of internet-based information gathering for active ageing are significant. Older adults are motivated to seek health related information online for a variety of reasons, including being self-reliant, staying active and productive, making better treatment choices, achieving a healthier life, valuing a healthy lifestyle, feeling relieved from stress, and lack of adequate information on health issues. They seek health information to maintain their independence, participate in decision-making about their care plan, and to stay healthy and active even in the face of physical limitations. However, this also exposes them to potential risks and ethical implications. The ethical implications of internet-based information gathering for active ageing must be carefully considered. For example, there is a concern about the quality and reliability of the health information available online which are further discussed in this study. As another example, privacy concerns arise as older adults may unknowingly share personal and sensitive information while seeking health information online. The study also focuses on the importance of addressing ethical implications and potential risks associated with internet-based information gathering for active ageing. In conclusion, exploring the potential benefits and ethical implications of internet-based information gathering in promoting active ageing is crucial in order to develop effective policies and programs to support the health and well-being of older adults.

Classification and identification of fruits and vegetables through image recognition technology has witnessed significant growth in recent years, and this advancement is driven by the importance of nutritional profiling in determining dietary recommendations, particularly for people managing diabetes. Various studies were done using image recognition technology to identify the images and create nutritional information on the scanned images. The nutritional information emphasized in all the previous studies were mainly calorie value and some included information on sodium, potassium, iron, calcium, vitamins, protein, fats, and carbohydrate. However, the existing literature falls short in comprehensively integrating image recognition of fruits and vegetables with their sugar content values and how it affects the blood glucose levels in individuals with diabetes. Therefore, this project aims to bridge these gaps and proposes a novel solution to educate individuals managing diabetes on nutritional values such as calorie value and sugar content in fruits and vegetables and how the consumption affects blood glucose levels through image recognition technology. It is carried out by creating a workflow that demonstrates image classification and recognition, as well as a web application in Jupyter Notebook using Python libraries. A Kaggle dataset which consists of 3825 images of fruits and vegetables was used in this project. Three pre-trained deep-learning models named AlexNet, MobileNet-v2, and YOLOv4 were chosen for the image classification as these models have given the highest accuracy percentage for fruits and vegetables image classification in previous literature. The AlexNet model has given an accuracy percentage of 48 %, whereas MobileNet-v2 has given an accuracy percentage of 94% upon training the dataset with 3825 images. Due to the limitation of hardware and labeling, the YOLOv4 model was not used to train the dataset for image classification.