Alzheimer's disease predictive tool based on multiple biomarkers and multiple risk factors formulae / Hana' Abd Razak - Abd Razak, Hana' (2011)
Alzheimer's disease (AD) is the most common form of dementia due to progressive mental deterioration. Reported recently, almost 50, 000 senior citizen aged 65 and above are affected with AD in Malaysia. This neurodegenerative dementia is serious but has no effective long-term treatment. The rapid development within the field of diagnostic measurement for AD observed in recent years offers new possibilities for scientists to invent more accurate tools in predicting susceptibility to the disease. Previous studies have seen the establishment of biomarkers as a significant method for predicting the risk or progression of AD. However, these considered the accuracy of one biomarker only. AD risk factors, on the other hand, have been a consideration in many studies but have not been properly modelled to be an advanced predictive tool. It is based on lifestyle choices that can help reduce the chance of developing the disease. Hitherto, wellestablished AD risk factors have never been considered for incorporation into a predictive tool and they only serve to indicate lifestyle choices that can help reduce the chance of developing the disease. In this work, a new predictive tool based on AD risk factors was developed. This is then incorporated into the existing biomarker predictive tools. A new mathematical model has been defined to describe the predictive tool based on disease profile that incorporates biomarkers and risk factors of AD. The tool has been demonstrated good accuracy of analysis for multiple factors related to AD. The formulae developed are based on the proportion of disease profile and several standard mathematical equations. The formulae were examined through three conditions of patients presenting; a) no biomarker with multiple risk factors, b) single biomarker with multiple risk factors, and c) multiple biomarkers with multiple risk factors. Findings in this work have shown that the developed predictive tool may offer the percentage of probability due to multiple biomarkers, multiple risk factors and single biomarker. It also reveals that the consideration in adding the risk factors as a predicting parameter could enhance the predicting result.