TYPE2HEART

WDC-MATH: Heart Failure Risk Score Predictor

Table of Contents

1. Introduction
2. Background
3. Project Overview
4. Methodology
5. Installation
6. Usage
7. Results and Interpretation
8. Future Work
9. Contributors
10. Acknowledgments
11. License

1. Introduction

Welcome to the WDC-MATH (Weight [BMI], Diabetes, Creatinine, Myocardial Infarction [MI], Age, High Blood Pressure, HDL-C) Heart Failure Risk Score Predictor project! Heart failure is a significant concern among patients with type-2 diabetes, high blood pressure, and advancing age. The recent discovery of a promising class of medications for preventing heart failure in diabetes patients has spurred the need for a reliable risk assessment tool. WDC-MATH aims to address this challenge by providing a robust risk score calculation that classifies patients according to their potential for heart failure.

2. Background

Type-2 diabetes, high blood pressure, and age are well-established risk factors for heart failure. The emergence of a new class of medications targeting heart failure prevention in diabetes patients has opened avenues for more effective management. However, identifying individuals at the highest risk of heart failure remains a complex task. This project builds upon scientific findings to develop a comprehensive risk score prediction model that integrates critical parameters.

3. Project Overview

WDC-MATH is a heart failure risk score predictor designed to assist healthcare professionals in identifying patients with diabetes who are at the highest risk of heart failure. The predictor derives its name from the key parameters it assesses: Weight (BMI), Diabetes, Creatinine, Myocardial Infarction (MI), Age, High Blood Pressure, and HDL-C (High-density lipoprotein cholesterol).

4. Methodology

The WDC-MATH predictor employs a multi-dimensional approach to calculate a risk score for each patient. It incorporates machine learning algorithms to analyze the relationships between the identified risk factors and their contribution to heart failure risk. The model is trained on a comprehensive dataset comprising patient profiles and heart failure outcomes. Through feature engineering, data preprocessing, and algorithmic training, WDC-MATH learns to quantify the risk associated with each parameter and produces a cumulative risk score.

5. Installation

To utilize the WDC-MATH Heart Failure Risk Score Predictor, follow these steps:

1. Clone the repository: git clone https://github.com/Zarak-Shah-ji/TYPE2HEART.git
2. Navigate to the project directory: cd TYPE2HEART
3. Install required dependencies: pip install -r requirements.txt

6. Usage

1. Prepare patient data in CSV format, ensuring it contains relevant parameters: Weight (BMI), Diabetes, Creatinine, Myocardial Infarction (MI), Age, High Blood Pressure, and HDL-C.
2. Execute the predictor script: python predict.py --input data.csv
3. The predictor will generate a risk score for each patient, categorizing them into low, moderate, or high-risk groups.

7. Results and Interpretation

The WDC-MATH predictor produces risk scores that help clinicians stratify patients into different risk categories. By identifying high-risk individuals, healthcare professionals can allocate resources more effectively, initiate preventive measures, and monitor these patients more closely. The results provide valuable insights for personalized treatment strategies, reducing the incidence of heart failure.

8. Future Work

The WDC-MATH project lays the foundation for ongoing research and development:

• Enhanced Data Collection: Expanding the dataset to include a broader demographic of patients and diverse geographic regions.
• Model Optimization: Continuously refining the machine learning algorithms to improve prediction accuracy and robustness.
• Integration with Healthcare Systems: Developing an interface for seamless integration of WDC-MATH into electronic health record systems.

9. Contributors

This project was made possible by the collaborative efforts of dedicated contributors:

• Wonjun Lee (Project Lead)
• Zarak Shah Ji (Data Researcher)
• Jahanzaib Malik (Data Researcher)

10. Acknowledgments

We would like to express our gratitude to the community for their valuable insights and guidance throughout the development of WDC-MATH.

11. License

This project is licensed under the MIT License. See the LICENSE file for details.

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Note: This README provides an elaborate overview of the WDC-MATH Heart Failure Risk Score Predictor project, highlighting its significance, methodology, usage instructions, and future prospects. The content is provided for illustrative purposes and can be further customized and expanded as needed.