Alzheimer's Detection

The project is to detect Alzheimer's disease using Machine Learning tool on MRI dataset.

We use 5 model of Machine learning (Logistic Regression, AdaBoost, xgboost, Random Forest and Decision Tree) and compare them with each other.

Installation

Create virtual environment

conda create -n Alzheimer python=3.8
conda activate Alzheimer

Install dependencies:

pip install -r requirements.txt

Download and set up data by running

bash setup_data.sh

Run and save model

python train.py

Expected output:

----------------Result--------------
model    F1_score    Precision  Recall

Make sure folder models (save best model) exists

Make sure your input is in exactly in order

Feature	Descripstion
`M/F`	Male of Female
`Age`	Age of patient
`EDUC`	Years of education
`SES`	Socioeconomic Status
`MMSE`	Mini Mental State Examination
`eTIV`	Estimated Total Intracranial Volume
`nWBV`	Normalize Whole Brain Volume
`ASF`	Atlas Scaling Factor

To make the prediction

  python Alzheime_Detector.py -i *8-Features-above

Each features seperates by comma( , )

The prediction of patient will be Demented or Nondemented

streamlit run streamlit.py

Then go to the local link and enter patient information

Press Make prediction button to get the result.