IMU University Learning Resources Repository

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.

This study examines the relationship between health expenditures, economic growth, and health outcomes in ASEAN region. It specifically looks at the impact on life ex­pectancy and infant mortality rates. Using annual dataset from 2000 to 2021 taken from the World Development Index by World Bank, the study applies econometric technique panel ARDL and Dumitrescu-Hurlin causality tests. The findings reveal a significant positive association between public and private health expenditures with life expectancy in the long term, emphasizing the critical role of government spending in improving health outcomes, with the complementing support of the private sectors. However, the mixed impacts of public health expenditure on infant mortality rates sug­gest potential inefficiencies and mis-allocations in health spending. There is a clear link between economic growth with increased life expectancy and infant mortality reduc­tion, which highlights the importance of sustained economic development for better health outcomes. The study also identifies bidirectional causality between GDP per capita and life expectancy. In order to achieve significant improvements in population health, it is crucial to make strategic health investments. Policy implications include the need for targeted policies to address healthcare financing and service delivery dis­parities in ASEAN countries. Governments should effectively allocate health expenditure to ensure equitable access to healthcare services, particularly for vulnera­ble populations. Future research should focus on examining the impact of health interventions and policies, by including other health indicators and factors to gain more comprehensive view into the progress of UHC and to identify best practices. Keywords: health expenditure, economic growth, health outcome, ASEAN

Travel insurance is becoming more widely recognised as a critical financial safeguard against unforeseen travel-related complications on a global scale. However, its adoption rate in Malaysia remains below the global average, necessitating a deeper understanding of the contributing factors. This quantitative study investigates the behaviours and perceptions of Malaysian travellers, with a particular emphasis on the Klang Valley region, to provide actionable insights for boosting travel insurance adoption in Malaysia. This study investigates the impact of attitudes, subjective norms, and perceived behavioural control on the intention to purchase travel insurance, using the Theory of Planned Behaviour (TPB) as its foundation. Furthermore, it incorporates insurance literacy as an additional predictor into the TPB model. A bilingual online questionnaire was implemented to gather data from travellers in the Klang Valley, employing non-random sampling. The results indicate that demographic variables, including age, education, income, attitudes, risk perceptions, and travel insurance knowledge significantly influence purchase intentions. These insights can assist the insurance industry and policymakers in promoting travel insurance, thereby fostering socioeconomic development through the tourism sector.