Project information

• Category: Machine Learning Model
• Stack: Python, Sci-Kit, Panda, Numpy, Git
• Project Date: April 2022
• Github URL: https://github.com/jabhuiyan/AD-diagnosis

AD Diagnosis focuses on developing a Support Vector Machine (SVM) model for the diagnosis of Dementia of Alzheimer's Type (DAT) based on changes in brain glucose metabolism. A Support Vector Machine model was trained using the Radial Basis Function (RBF) kernel to capture complex relationships in the data. A second experiment employed the Polynomial kernel to account for non-linearities in glucose metabolism changes. The third experiment utilized the Linear kernel for a more straightforward representation of relationships between features.

The SVM model developed in this project holds significant importance in the field of Alzheimer's disease diagnosis because:

SVM models can effectively identify patterns in brain glucose metabolism changes, enabling early detection of DAT.

By leveraging different kernels, the model adapts to various patterns, enhancing precision and accuracy in diagnosis.

Early diagnosis facilitates timely intervention and informed treatment plans, potentially improving the quality of life for individuals with DAT.

The project contributes to a data-driven understanding of Alzheimer's disease, paving the way for further research and advancements in diagnostic methods.

Full report can be found in the github repo.