Alzheimer's disease is a complex neurodegenerative disorder that affects millions of people worldwide. Early detection and prediction of Alzheimer's can lead to better management and treatment outcomes. This prediction system utilizes a machine learning model trained on a dataset of relevant features to provide predictions about the likelihood of Alzheimer's disease.
The purpose of this project proposal is to develop a machine learning model for the early prediction of Alzheimer's disease. Alzheimer's disease is a devastating neurodegenerative disorder that affects millions of individuals worldwide. The potential impact of this project on the issue of Alzheimer's disease is significant:
- Early prediction of Alzheimer's disease can lead to timely interventions, potentially slowing down the progression of the disease.
- Accurate prediction models can aid in identifying suitable candidates for clinical trials and research studies.
- Providing a tool for early prediction can raise awareness about Alzheimer's disease and encourage individuals to seek early medical evaluation.
The model was trained on a dataset collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) . This dataset is a comprehensive collection of clinical, imaging, and genetic data from individuals with Alzheimer's disease.
- Logistic Regression
- Random Forest Classification
- CNN For MRI scans of the human brain (Still in development)