There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Azian Azamimi Abdullah |
| Institution: | Universiti Malaysia Perlis |
| Department: | Faculty of Electronic Engineering Technology, |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease (AD) is the most frequent incurable neurodegenerative disease, a general term for memory loss and other cognitive abilities. Early detection of AD can help with proper treatment and prevent brain tissue damage. Traditional medical tests are time consuming, fail to recognize early signs and lack of diagnosis sensitivity and specificity. To achieve promising prediction accuracy, the best predictive machine learning model is selected based on initial pre-processing step followed by vital attributes selection and performance evaluation of our proposed supervised machine learning algorithms. Finally, Graphical User Interface (GUI) prediction tool will be developed through prediction over ADNI dataset that have been pre-processed. |
| Additional Investigators | |
| Investigator's Name: | Aw Hui Yee |
| Proposed Analysis: | Alzheimer’s disease (AD) is the common neurodegenerative disease that causes the impairment of the brain tissue with a gradual decline in the memory and thinking skills among the elderly population. However, there is a lack of reliable biomarker and effective medicine treatment to detect and cure the AD respectively. Hence, this project is focusing on the implementation of the supervised machine learning algorithms on the prediction of AD based on the implementation of data preprocessing, Exploratory Data Analysis (EDA) and feature selection. The machine learning with feature selection, Lasso Regularization performed much better than those without feature selection. Thus, the Graphical User Interface (GUI) is then developed to provide convenience for the user to accurately predict their current AD’s status. |
