## Overview

This repository contains the code and documentation for Assignment 1 of SPH-6004, where we build a predictor that estimates the patients' risk of kidney failure in the Intensive Care Unit (ICU). The assignment focuses on developing a predictive model using clinical data to help identify patients at higher risk of kidney failure, enabling early intervention and improved patient outcomes.

## Dataset

The dataset used for this assignment is sourced from MIMIC-IV. It contains de-identified health-related data of over forty thousand patients who stayed in critical care units at the Beth Israel Deaconess Medical Center, which are used to train and evaluate the predictive model.

## Methodology

- **Data Preprocessing**: The dataset underwent preprocessing steps such as handling missing values, encoding categorical variables, and scaling numerical features.

- **Model Selection**: Several machine learning models were considered and evaluated for their performance in predicting kidney failure risk. Models included Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine.

- **Model Training and Evaluation**: The selected model was trained on the preprocessed data and evaluated using appropriate metrics such as accuracy, precision, recall, and F1-score.

## Repository Content

- `Assignment1_code.ipynb`: Contains data preprocessing and all model implementation.

- `Experimental Results.png`: Table that shows model performance.

- `Experimental Setup.png`: Flowchart for model architecture.

## Contributors

- LIN KUNSHI