Health Risk Analyzer Application

Author: Anastasia Koiava
Co-authors: Anastasia Tchelidze, Ana damadaeva
Keywords: Research, Logistic Regression, Disease Risk, Diabetes, Databases
Annotation:

This study, based on customer surveys and machine learning, analyzes the risk of various diseases considering factors such as age, gender, blood sugar levels, BMI, and smoking. We use logistic regression to identify connections between certain physical characteristics and severe diseases, such as diabetes. The web application is primarily based on Python (data cleaning, logistic regression processing, and a backend built using Flask), while the front end is built with "vanilla" HTML/CSS/JavaScript. The application is not complete due to the lack of similar data sources in Georgia, and its implementation/development can be achieved through the enrichment/acquisition/implementation of Georgian databases.