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: | Viren Narvekar |
| Institution: | Symbiosis Institute of Technology |
| Department: | Electronics and Tele-Communication |
| Country: | |
| Proposed Analysis: | Using Data provided in ADNI, our goal is to develop a computer model that assists in the diagnosis of AD. Using multiple models as naïve Bayesian, KNN, random forest, Decision Trees and SVM and compare the accuracy and the computational times required by each algorithm to determine the best and fast algorithm. A learning model that can effectively predict and segregate true AD subjects from a given population. Requires Pre-processing and extracted features will be inserted in ML classifiesr and validate its performance. Our model has been divided into predictive and evaluation which is for predicting the AD via dataset collection. The ML model pipeline approach was applied in the diagnosis of AD, to classify true dementia subjects. The proposed ML framework can learn data by the provided classifiers and categorize them as true and non-AD subjects. The Jupyter platform with Python libraries was used for an experimental setup; this platform is well known by developers for processing, assessment, and model building. |
| Additional Investigators |
