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: | Tekumudi Vivek Sai Surya Chaitanya |
| Institution: | Amrita Vishwa Vidyapeetham |
| Department: | Computer Science (Artificial Intelligence) |
| Country: | |
| Proposed Analysis: | I am requesting the ADNI dataset to conduct a research study on the early detection and prediction of Alzheimer's disease using machine learning techniques. The proposed analysis involves the following steps: Data Preprocessing: Cleaning and preparing the ADNI dataset for analysis, including handling missing values and standardizing data formats. Feature Engineering: Extracting relevant features from the dataset, such as neuroimaging measures, cognitive scores, demographic information, and genetic markers. Model Selection: Evaluating and comparing different machine learning algorithms, such as support vector machines, random forests, and deep learning models, to identify the most suitable approach for Alzheimer's disease prediction. Model Training and Validation: Implementing the selected machine learning models on the preprocessed data and conducting cross-validation to assess their performance and generalization capability. Predictive Analysis: Utilizing the trained models to predict the risk of Alzheimer's disease in individuals from the ADNI dataset and evaluating the accuracy, sensitivity, specificity, and other relevant metrics. Interpretation of Results: Interpreting the model outcomes and identifying key features contributing to Alzheimer's disease prediction. Ethical Considerations: Ensuring the ethical use of the data and adhering to all relevant data sharing and privacy regulations. The primary objective of this analysis is to develop a robust and accurate predictive model for early detection of Alzheimer's disease, which can assist in providing timely interventions and personalized care for at-risk individuals. The research findings will contribute to the growing body of knowledge in the field of Alzheimer's disease and may have potential implications for improving diagnostic strategies and patient outcomes. |
| Additional Investigators |
