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. Early detection is crucial for better patient care and the development of potential interventions. This project aims to leverage machine learning techniques to create a predictive model that can identify individuals at risk of Alzheimer's disease based on relevant data.
Dataset
The model was trained on a dataset collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) . The ADNI dataset is a comprehensive collection of clinical, imaging, and genetic data from individuals with Alzheimer's disease. Using data provided by the ADNI Project, it is our goal to develop a computer model that assists in the diagnosis of the disease. We'll try multiple models recently popularized in machine learning (Logistic Regression, Random Forest, MLP).
Techniques
This project will engage a combination of machine learning techniques and data preprocessing methods to develop an accurate Alzheimer's disease prediction model:
Data Preprocessing
Data cleaning and preprocessing to handle missing values and outliers.
Feature selection to identify the most relevant variables for prediction.
Machine Learning Models
Utilize various machine learning algorithms:
Logistic Regression
Random Forest
MLP Classifier
Hyperparameter tuning and cross-validation to optimize model performance.
Potential Impact
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.
Alzheimer Prediction System #11
Purpose
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. Early detection is crucial for better patient care and the development of potential interventions. This project aims to leverage machine learning techniques to create a predictive model that can identify individuals at risk of Alzheimer's disease based on relevant data.
Dataset
The model was trained on a dataset collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) . The ADNI dataset is a comprehensive collection of clinical, imaging, and genetic data from individuals with Alzheimer's disease. Using data provided by the ADNI Project, it is our goal to develop a computer model that assists in the diagnosis of the disease. We'll try multiple models recently popularized in machine learning (Logistic Regression, Random Forest, MLP).
Techniques
This project will engage a combination of machine learning techniques and data preprocessing methods to develop an accurate Alzheimer's disease prediction model:
Data Preprocessing
Data cleaning and preprocessing to handle missing values and outliers. Feature selection to identify the most relevant variables for prediction.
Machine Learning Models
Potential Impact
The potential impact of this project on the issue of Alzheimer's disease is significant: