Healthcare Patient Risk Analytics & Diagnostic System

Healthcare Patient Risk Analytics & AI Diagnostic Desktop Application

A predictive medical analytics desktop application engineered in Python to assist healthcare professionals in evaluating patient health risks using Gradient Boosting machine learning models.

Key Features:

Interactive Diagnostic Interface (Tkinter GUI): Input clinical indicators (BMI, blood pressure, glucose levels, lifestyle metrics) for instant risk assessment.

Clinical Data Intelligence: Embedded Matplotlib and Seaborn analytics visualizing clinical metrics distribution and kernel density estimation (KDE).

Machine Learning Pipeline: Integrated ensemble classification algorithms (Gradient Boosting) trained on processed patient diagnostic metrics.

Tech Stack: Python, Tkinter, Pandas, Scikit-Learn, Matplotlib, Seaborn.