📊 COVID-19 prediction

This project aims to predict the result of three kinds of exams that detects the viral disease COVID-19: PCR, IgM and IgG tests.

It trains three feedforward neural networks with data from exams of the Albert Einstein Israelite Hospital, obtained from the COVID-19 Data Sharing/BR, available at https://repositoriodatasharingfapesp.uspdigital.usp.br/. The preprocessed data can be found at data/einstein.out.csv.

This is a project for the subject MAC0425 (artificial intelligence) at IME-USP. The final report (in Portuguese) can be found here.

Instalation

All the packages used can be installed with:

  pip install -r requirements.txt

The three models can be trained with:

  python3 main.py

The models were validated by Cross-Validation with k-folds (k=10). The results are shown below.

Repetition	PCR	IgM	IgG
1	0.644	0.568	0.639
2	0.715	0.542	0.604
3	0.701	0.600	0.500
4	0.500	0.570	0.613
5	0.738	0.653	0.670
6	0.704	0.542	0.549
7	0.723	0.649	0.500
8	0.682	0.640	0.678
9	0.736	0.625	0.610
10	0.716	0.566	0.577

Metric	PCR	IgM	IgG
mean	0.686	0.595	0.594
std. dev.	0.071	0.044	0.063
max	0.738	0.653	0.678
min	0.500	0.542	0.500