caiselvass / survival-prediction-cirrhosis

Machine learning project to predict survival outcomes for cirrhosis patients using K-Nearest Neigbors (KNN), Decision Tree, and Support Vector Machine (SVM) models, based on UCI's clinical dataset.

Survival Prediction for Cirrhosis Patients

This repository contains the code and analysis for the project aimed at predicting the survival of cirrhosis patients using machine learning models such as K-Nearest Neighbors (KNN), Decision Tree and Support Vector Machine (SVM). The dataset used in this study is sourced from the University of California, Irvine (UCI).

Dataset

The dataset includes 17 clinical features of cirrhosis patients and is utilized to predict patient survival. Detailed information about the dataset and its features can be found at UCI Machine Learning Repository.

Analysis and Model

For a comprehensive explanation of the data preprocessing, analysis, and model development and selection, refer to the provided PDF document in the repository (Informe Pràctica IAA - Cai Selvas Sala.pdf).

Python and Library Requirements

The project is developed using Python 3.x. All dependencies required to run the project are listed in the requirements.txt file included in the repository.