Predicting patient who is at risk of developing Hypertension or Diabetic using Shiny4Python

This is a Machine learning/Artificial intelligence EHR Software I built using Shiny for Python to help physicians predict each patient who is at risk of developing Hypertension or Diabetes using various machine learning models in a resource-limited setting. The app leverages supervised machine learning models to predict whether a patient is at risk of developing hypertension or diabetes. The app includes a variety of machine learning models and statistical techniques for data exploration, providing valuable insights for healthcare professionals.

The use of this software includes:

Severed as an Electronic Health Record

“Register UI”

Data Retrieval Tab

The Data Retrieval Tab allows users to interact with the database to retrieve or delete data based on specific conditions. Users can apply conditional filtering to extract relevant patient data for analysis and decision-making.

Data Exploratory Tab

The Data Exploratory Tab of the app allows users to explore statistical models for analyzing the dataset. Each model provides unique insights into the data distribution, relationships, and predictive power. data exploratory analysis using the following statistical models:


The doctor/Physician will not input data but instead search for a patient using their unique patient ID in the search box. After entering the patient ID, the doctor can click on the predict button to receive the prediction result for that specific patient. This streamlined process allows doctors to quickly access and analyze individual patient predictions without the need for manual data entry.

The app includes the following supervised machine-learning models for prediction:

Data Training-Test Split:

Dashboard Section: A comprehensive dashboard that offers various interactive components for managing and analyzing patient data effectively. The dashboard includes allow following key elements:

Video demo:

This is the link for the App: