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: | Minkyu Ahn |
| Institution: | Handong Global University |
| Department: | Electrical Engineering and Computer Science |
| Country: | |
| Proposed Analysis: | Random forest (RF) is not used actively to predict Alzheimer's disease with brain MRIs. Recent studies have reported RF's effectiveness in predict AD, but the test sample sizes were too small to draw any solid conclusions. RF is a bagging ensemble model and has many important advantages, such as robustness to noise, and effective structure for complex multi-modal data and parallel computing, and also provides important features that help investigate biomarkers. Thus, it is timely to compare RF with other learning model methods, including deep learning, particularly with large amount of data. |
| Additional Investigators | |
| Investigator's Name: | Minseok Song |
| Proposed Analysis: | Random forest (RF) is not used actively to predict Alzheimer's disease with brain MRIs. Recent studies have reported RF's effectiveness in predict AD, but the test sample sizes were too small to draw any solid conclusions. RF is a bagging ensemble model and has many important advantages, such as robustness to noise, and effective structure for complex multi-modal data and parallel computing, and also provides important features that help investigate biomarkers. Thus, it is timely to compare RF with other learning model methods, including deep learning, particularly with large amount of data. |
