This project focuses on the analysis of Alzheimer's disease data using deep learning techniques. Our objective is to develop a predictive model that can identify early indicators of Alzheimer's, thereby aiding in timely diagnosis and effective treatment planning.
- Objective: Utilize deep learning to analyze Alzheimer's data for early prediction.
- Data Source: Diverse datasets including patient medical histories, genetic information, and neuroimaging data.
- Methods: Advanced data processing and deep learning modeling.
To set up this project, follow these steps:
- Clone the repository:
git clone https://github.com/anaschougle32/Alzhimer-s-Data-Analysis
- Open the Jupyter notebooks in the project to view the analysis.
- Run the notebooks to execute the data processing and model training steps.
- Extensive data cleaning and preprocessing to structure the data for model input.
- Exploratory data analysis to understand trends and patterns in Alzheimer's progression.
- Development of a deep learning model tailored for Alzheimer's prediction.
- Training and validation of the model using the preprocessed datasets.
- Handling the complexity of Alzheimer's data: Implemented advanced data processing techniques.
- Ensuring model accuracy: Thorough validation and testing with diverse data.