COVID-19 Prediction based on Symptoms

Via Random Forest model

Covid Detection model

I have created an ML model which can predict if a person has covid or not using symptoms they have. It is a supervised Classification model (Random forest model) developed using python. The model has a 96.7% Accuracy.

About COVID-19

>> From WHO website

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and become seriously ill or die at any age.

Dataset

The data for developing the model was taken from Here

This dataset was cleaned and Classification performed using python in a Jupyter notebook. The cleaned data set can be found in data folder and is saved as data.csv. You can also find it by clicking here

Classification model

Classification was done using logistic regression and Random forest and the following confusion matrix was obtained.

The Accuracy, Precision and recall along with F1 score were better for the random forest model, and so I went with it.

Python Scripts

Random_forest.py

This python program creates the random forest model using the data.csv dataset and saves it as a joblib file.

Predictions.py

This python program accepts user information and uses it to predict Covid 19 condition of the user using the created model.