Collaborators: Hwayeon Kang, Minha Kim
Selected Theme: HealthCare - Health Equity
This study explores the correlation between lung cancer survival rates and societal factors on a global scale. While existing research has linked lung cancer to environmental pollutants and resource accessiblity in the U.S., little is known about how broader societal influences contribute to varied health outcomes worldwide. Through a comprehensive analysis of diverse variables, including healthcare accessibility, socio-economic conditions, and cultural factors, this research aims to uncover patterns and relationships that can deepen our understanding of lung cancer survival dynamics. Our machine learning approach empowers users to predict lung cancer mortality in various hypothetical scenarios, utilizing seven socio-economic factors.
By investigating the interplay between lung cancer outcomes and societal factors, this research seeks to inform public health policies and resource allocation strategies. The study's findings may offer valuable insights into addressing the complexities of lung cancer on a global level, ultimately contributing to more effective interventions and equitable health outcomes.
URL:
https://lunglink-hub.onrender.com
Installation Instructions:
You need to install the following first
- Flask==3.0.0
- gunicorn==21.2.0
- sqlalchemy==2.0.23
- pymysql==1.1.0
- scikit-learn==1.3.2
- pandas==2.1.4
Usage Guidelines:
We offer three main pages on our platform: Discover Insights, Machine Learning, and Statistics.
- Discover Insights: This page allows you to simulate hypothetical scenarios for predicting lung cancer mortality by inputting seven socio-economic factors.
- Machine Learning: Here, you can delve into the machine learning methods used for prediction, explore the performance evaluation of various ML techniques, and understand why we have chosen Adaboost.
- Statistics: On this page, you can explore charts depicting the seven factors by country using a search tool. Additionally, we provide global maps for both male and female populations, highlighting crucial insights for public health interventions, resource allocation, and targeted cancer prevention strategies. These visualizations contribute to global efforts to mitigate the impact of lung cancer and enhance overall health outcomes on a worldwide scale.
- Lung Cancer Survival Rates : https://gco.iarc.fr/overtime/en/dataviz/trends
- Proportion of Population Pushed Below 3.65 Poverty Line by Out-of-Pocket Health Care Expenditure : https://data.worldbank.org/indicator/SH.UHC.NOP2.ZS
- Forest Area Coverage : https://data.worldbank.org/indicator/AG.LND.FRST.ZS
- Current Health Expenditure (% of GDP) : https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS
- Domestic General Government Health Expenditure per Capita (Current US Dollars) : https://data.worldbank.org/indicator/SH.XPD.GHED.PC.CD
- Out-of-Pocket Expenditure per Capita (Current US Dollars) : https://data.worldbank.org/indicator/SH.XPD.OOPC.PC.CD
- Physicians (Per 1000 People) : https://data.worldbank.org/indicator/SH.MED.PHYS.ZS
- Incidence of Tuberculosis (Per 100,000 People) : https://data.worldbank.org/indicator/SH.TBS.INCD
- Urban Population (% of Total Population) : https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS"
- HTML : https://codepen.io/alliwagner/pen/nOPZob, https://codepen.io/cobra_winfrey/pen/OJRjBoj