S1-B14-Deep Sai Gilla-Alzheimer's disease prediction

deepsaigilla commented 1 month ago

This description provides a comprehensive overview of the Alzheimer's Disease detection project, including setup instructions and a summary of the notebook's contents. Please let me know if there are any specific details or sections you would like to add or modify!

Alzheimer's Disease Detection Project

This project uses machine learning techniques to detect Alzheimer's Disease based on medical imaging and other relevant data. The main focus is on implementing and evaluating different models to improve the accuracy of diagnosis.

Project Overview

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early detection is crucial for managing the disease and improving patient outcomes. This project aims to leverage machine learning techniques to analyze medical imaging data and other relevant features to detect Alzheimer's Disease.

Dataset

The dataset used in this project includes medical imaging data and other features relevant to Alzheimer's Disease diagnosis. The data is preprocessed and split into training and testing sets to evaluate the performance of different machine-learning models. Link to the dataset: Augmented Alzheimer MRI Dataset

Installation

To run the code in this repository, you must install the necessary dependencies. You can do this by running the following command:

Run the code:

pip install -r requirements.txt

Usage

To use this project, follow these steps:

Clone this repository to your local machine.

Install the required dependencies as mentioned in the Installation section.

Open the Jupyter notebook Alzheimer.ipynb to explore the code. python Alzheimer.ipynb jupyter notebook Alzheimer.ipynb Notebook Contents The Jupyter notebook Alzheimer.ipynb contains the following sections:

Introduction:

Overview of the project and its objectives. Data Preprocessing: Steps to clean and prepare the data for analysis. Exploratory Data Analysis (EDA): Visualizations and statistical analysis of the dataset. Model Implementation: Implementation of various machine learning models. Model Evaluation: Evaluation of the models using appropriate metrics. Conclusion: Summary of findings and future work.

Results

The results of this project include the performance metrics of different machine learning models used for Alzheimer's Disease detection. These metrics help in understanding the effectiveness of each model and identifying the best approach for early diagnosis.