You will get AI-Powered Ejection Fraction Prediction app from ECG Images
Project details
This project provides a deep learning-based system where users can simply open the application, upload an ECG image, and instantly receive key cardiac insights such as ejection fraction. Traditional methods are often expensive, time-consuming, and require expert interpretation, whereas this solution offers a fast, automated, and accessible alternative.
The system is designed to be scalable, and with sufficient data, it can be extended to predict additional cardiac markers such as troponin levels, CMR-related insights, and more detailed EF values directly from ECG data. This makes it a powerful foundation for future AI-driven cardiovascular diagnostic tools and research applications
The system is designed to be scalable, and with sufficient data, it can be extended to predict additional cardiac markers such as troponin levels, CMR-related insights, and more detailed EF values directly from ECG data. This makes it a powerful foundation for future AI-driven cardiovascular diagnostic tools and research applications
Machine Learning Tools
BERT, ChatGPT, Google Sheets, Keras, KNIME, NLTK, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$100
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 2 | 2 | 5 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 5 | 7 | 10 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $100
Additional Graph/Chart
(+ 1 Day)
+$3
Data Source Connectivity
(+ 1 Day)
+$10Frequently asked questions
About Shoaib
Machine Learning | Deep Learning | Data Science | Python
Chitral, Pakistan - 2:52 am local time
I follow a complete end-to-end project structure to ensure high-quality and maintainable solutions:
My Project Workflow:
✔ Business Understanding & Requirement Analysis
Understanding client goals, project requirements, and problem statements.
✔ Data Ingestion
Collecting data from multiple sources such as CSV files, databases etc
✔ Data Transformation & Preprocessing
Handling missing values, feature engineering, data cleaning, encoding, scaling, and preparing data for model training.
✔ Exploratory Data Analysis (EDA)
Analyzing trends, patterns, correlations, and generating meaningful insights using visualization tools.
✔ Model Development
Building and training Machine Learning and Deep Learning models using frameworks like Scikit-learn, TensorFlow, and PyTorch.
✔ Model Evaluation & Optimization
Improving model performance using hyperparameter tuning, cross-validation, and performance metrics.
✔ Deployment & Integration
Deploying models using Flask, FastAPI, Streamlit, Docker, or cloud platforms for real-world usage.
✔ Monitoring & Maintenance
Ensuring the system remains accurate, scalable, and production-ready over time.
Technologies & Skills:
Python
Machine Learning
Deep Learning
Data Science
Data Visualization
SQL & Databases
Flask / FastAPI / Streamlit
TensorFlow / PyTorch / Scikit-learn
What I have done So far:
I have also developed a healthcare AI application that predicts Ejection Fraction using ECG data, helping medical experts reduce the waiting time for Echocardiography reports and improve diagnosis efficiency.
I am also available for online training and mentorship in Data Science, Machine Learning, Deep Learning, Programming, and Data Visualization from beginner to advanced level.
Steps for completing your project
After purchasing the project, send requirements so Shoaib can start the project.
Delivery time starts when Shoaib receives requirements from you.
Shoaib works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Planning
Understand project goals, data format, and expected outputs (EF prediction and future extensions).
Data Collection & Preparation
Gather ECG image dataset, clean data, and prepare labels for model training.