Comparative Analysis of Machine Learning Algorithms for Vital Signs

This repository contains the implementation of three machine learning algorithms applied to a vital signs dataset, developed as academic work for the Intelligent Systems course.

Project Description

The project presents a comparative analysis between three distinct machine learning approaches:

• ID3 (Symbolic Learning - Decision Trees)
• Random Forest (Ensemble Learning)
• Neural Networks (Multilayer Perceptron)

Academic Paper

The complete work is documented in the scientific paper available in ARTIGO_2_SI.pdf, following the SBC template.