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: | Jadesola Osinowo |
| Institution: | Loughborough University |
| Department: | Department od Digital Technologies |
| Country: | |
| Proposed Analysis: | Introduction: Alzheimer's disease is a neurodegenerative disorder that causes cognitive and behavioral problems, eventually leading to the loss of independent living. Currently, there is no cure for Alzheimer's disease, and treatments are mainly focused on managing symptoms. The ability to predict Alzheimer's disease progression accurately can improve patient outcomes by enabling early interventions, better resource allocation, and personalized treatment plans. Reinforcement learning is a branch of machine learning that is suitable for solving sequential decision-making problems. Reinforcement learning involves learning to make decisions by receiving feedback in the form of rewards or penalties. This feedback is used to adjust the decision-making process, leading to better outcomes over time. In this dissertation proposal, we will investigate the use of reinforcement learning for predicting Alzheimer's disease progression. Specifically, we will compare the performance of different reinforcement learning algorithms and evaluate their ability to predict disease progression accurately. Literature Review: Previous research has shown that machine learning models, including deep learning, can be effective in predicting Alzheimer's disease progression (Wang et al., 2018). However, there is limited research on the use of reinforcement learning for predicting Alzheimer's disease progression. Reinforcement learning is a promising approach because it can model the decision-making process of Alzheimer's disease patients, which is influenced by various factors, including genetics, lifestyle, and environment. Reinforcement learning algorithms, such as Q-learning, deep Q-networks, and actor-critic, have been used in various applications, including robotics, gaming, and healthcare. However, there is limited research on the application of reinforcement learning in Alzheimer's disease prediction. Research Question: The research question for this dissertation is: Can reinforcement learning algorithms accurately predict Alzheimer's disease progression? Specifically, we will compare the performance of different reinforcement learning algorithms and evaluate their ability to predict disease progression accurately. Objectives: The objectives of this dissertation are: 1. To review the literature on the use of machine learning in Alzheimer's disease prediction. 2. To develop a reinforcement learning framework for predicting Alzheimer's disease progression. 3. To compare the performance of different reinforcement learning algorithms in predicting Alzheimer's disease progression. 4. To evaluate the accuracy of reinforcement learning algorithms in predicting Alzheimer's disease progression. Methodology: The methodology for this dissertation will involve the following steps: 1. Data Collection: We will use the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes clinical, imaging, and genetic data from Alzheimer's disease patients. This dataset will be used to train and evaluate our reinforcement learning models. 2. Reinforcement Learning Framework: We will develop a reinforcement learning framework for predicting Alzheimer's disease progression. This framework will include a state representation, action space, reward function, and learning algorithm. 3. Reinforcement Learning Algorithms: We will compare the performance of different reinforcement learning algorithms, including Q-learning, deep Q-networks, and actor-critic, in predicting Alzheimer's disease progression. We will evaluate the accuracy of these algorithms using performance metrics, including precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. 4. Evaluation: We will evaluate the accuracy of our reinforcement learning models in predicting Alzheimer's disease progression using the ADNI dataset. We will compare the performance of our models to existing machine learning models for Alzheimer's disease prediction. Conclusion: In conclusion, this dissertation proposes investigating the use of reinforcement learning for predicting Alzheimer's disease progression. The focus will be on comparing the performance of different reinforcement learning algorithms and evaluating their ability to predict disease progression accurately. The results of this research can help improve patient outcomes by enabling early interventions, better resource allocation, and personalized treatment plans. The proposed methodology involves data collection, developing a reinforcement learning framework, comparing the performance of different reinforcement learning algorithms, and evaluating the accuracy of our models. The findings of this research can |
| Additional Investigators | |
| Investigator's Name: | Jadesola Osinowo |
| Proposed Analysis: | Introduction: Alzheimer's disease is a neurodegenerative disorder that causes cognitive and behavioral problems, eventually leading to the loss of independent living. Currently, there is no cure for Alzheimer's disease, and treatments are mainly focused on managing symptoms. The ability to predict Alzheimer's disease progression accurately can improve patient outcomes by enabling early interventions, better resource allocation, and personalized treatment plans. Reinforcement learning is a branch of machine learning that is suitable for solving sequential decision-making problems. Reinforcement learning involves learning to make decisions by receiving feedback in the form of rewards or penalties. This feedback is used to adjust the decision-making process, leading to better outcomes over time. In this dissertation proposal, we will investigate the use of reinforcement learning for predicting Alzheimer's disease progression. Specifically, we will compare the performance of different reinforcement learning algorithms and evaluate their ability to predict disease progression accurately. Literature Review: Previous research has shown that machine learning models, including deep learning, can be effective in predicting Alzheimer's disease progression (Wang et al., 2018). However, there is limited research on the use of reinforcement learning for predicting Alzheimer's disease progression. Reinforcement learning is a promising approach because it can model the decision-making process of Alzheimer's disease patients, which is influenced by various factors, including genetics, lifestyle, and environment. Reinforcement learning algorithms, such as Q-learning, deep Q-networks, and actor-critic, have been used in various applications, including robotics, gaming, and healthcare. However, there is limited research on the application of reinforcement learning in Alzheimer's disease prediction. Research Question: The research question for this dissertation is: Can reinforcement learning algorithms accurately predict Alzheimer's disease progression? Specifically, we will compare the performance of different reinforcement learning algorithms and evaluate their ability to predict disease progression accurately. Objectives: The objectives of this dissertation are: 1. To review the literature on the use of machine learning in Alzheimer's disease prediction. 2. To develop a reinforcement learning framework for predicting Alzheimer's disease progression. 3. To compare the performance of different reinforcement learning algorithms in predicting Alzheimer's disease progression. 4. To evaluate the accuracy of reinforcement learning algorithms in predicting Alzheimer's disease progression. Methodology: The methodology for this dissertation will involve the following steps: 1. Data Collection: We will use the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes clinical, imaging, and genetic data from Alzheimer's disease patients. This dataset will be used to train and evaluate our reinforcement learning models. 2. Reinforcement Learning Framework: We will develop a reinforcement learning framework for predicting Alzheimer's disease progression. This framework will include a state representation, action space, reward function, and learning algorithm. 3. Reinforcement Learning Algorithms: We will compare the performance of different reinforcement learning algorithms, including Q-learning, deep Q-networks, and actor-critic, in predicting Alzheimer's disease progression. We will evaluate the accuracy of these algorithms using performance metrics, including precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. 4. Evaluation: We will evaluate the accuracy of our reinforcement learning models in predicting Alzheimer's disease progression using the ADNI dataset. We will compare the performance of our models to existing machine learning models for Alzheimer's disease prediction. Conclusion: In conclusion, this dissertation proposes investigating the use of reinforcement learning for predicting Alzheimer's disease progression. The focus will be on comparing the performance of different reinforcement learning algorithms and evaluating their ability to predict disease progression accurately. The results of this research can help improve patient outcomes by enabling early interventions, better resource allocation, and personalized treatment plans. The proposed methodology involves data collection, developing a reinforcement learning framework, comparing the performance of different reinforcement learning algorithms, and evaluating the accuracy of our models. The findings of this research can |
