Understanding the IT-related attitudes and needs of persons with age-related macular degeneration:a case study

In the UK, 20 per cent of people aged 75 years and over are living with sight loss; this percentage is expected to increase as the population ages (RNIB, 2011). Age-Related Macular Degeneration (AMD) is the UK’s leading cause of severe visual impairment amongst the elderly. It accounts for 16,000 blind/partial sight registrations per year and is the leading cause of blindness among people aged 55 years and older in western countries (Bressler, 2004). Our ultimate goal is to develop an assistive mobile application to support accurate and convenient diet data collection on which basis to then provide customised dietary advice and recommendations in order to help support individuals with AMD to mitigate their ongoing risk and retard the progression of the disease. In this paper, we focus on our knowledge elicitation activities conducted to help us achieve a deep and relevant understanding of our target user group. We report on qualitative findings from focus groups and observational studies with persons with AMD and interviews with domain experts which enable us to fully appreciate the impact that technology may have on our intended users as well as to inform the design and structure of our proposed mobile assistive application.

Bagging model with cost sensitive analysis on diabetes data

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.