Ruihan Dou (rd593), Anqi Li (al2555)
Background: In 2023, heart disease ranked as the leading cause of death in the United States, according to CDC. The suddenness of heart disease and the potential for rapid death without timely treatment underscore the importance of early risk assessment. The CDC has identified several major contributors to heart disease, including high blood pressure, smoking, diabetes, alcohol drinking, high BMI, and various other factors. This project aims to quantify the impact of these factors on an individual's susceptibility to heart disease. Python will be utilized throughout the project for model development and data visualization.
Goal: This project aims to identify the key indicators contributing to heart disease risk and develop predictive models to determine whether an individual is vulnerable to a heart disease. In this project, we will conduct exploratory data analytics and feature engineering to investigate various indicators. Additionally, we will build classification models, including K-Nearest Neighbors (KNN), logistic regression, Support Vector Machine (SVM), and Neural Networks, as taught in ORIE 5741.
Significance: The significance of this project lies in its potential to address a critical public health issue and drive healthcare innovation. By developing accurate predictive models for measuring heart disease, this project contributes to the early detection and prevention of the leading cause of death in the United States. The actionable insights derived from the project will empower individuals to take proactive measures in managing their heart health, potentially reducing the burden on healthcare systems and saving lives. Moreover, the project's findings can be leveraged to develop innovative healthcare solutions, such as personalized health management insurances or targeted interventions, revolutionizing the approach to heart disease prevention.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent