Symptom Based Disease Prediction - DWDM Project

Dataset - Disease Prediction Using Machine Learning

Available at: https://www.kaggle.com/datasets/kaushil268disease-prediction-using-machine-learning

Total 4932 data objects. Each with 132 attributes that represent binary values to denote presence of symptom.

The labels are 42 different diseases, in nominal form.

Models Used

• Support Vector Classifier
• Guassian Naive Bayes
• Neural Network with 2 Hidden Dense Layers
• Decision Tree Classifier
• Random Forest Ensemble Classifier

Used for PCA for unsupervised data attribute reduction.

Usage

1 . Create a Python virtual environment

python3 -m venv venv

2. Activate environment

source venv/bin/activate

3. Install dependencies from the given requirements.txt fiel

pip install -r requirements.txt

4. Run python file. Uncomment code lines to use different models.

python main.py

Or refer to notebooks for prerun code.

Group Members

Wajeeha Aftab, Ayesha Inam, Fatima Kashif, Muhammad Arham