FetalAI: Using Machine Learning To Predict And Monitor Fetal Health

FetalAI employs machine learning to predict and monitor fetal health, improving prenatal care and enabling early detection of complications.

40 Hrs. | Intermediate

INR 0

This Course Includes:

  • 30 Days Access to Workspace
  • Dedicated Mentor Support
  • Project Resources & References
  • Project Completion Certificate
Skills you will develop

  • Python
  • Machine Learning
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn
  • HTML
  • Python-Flask
Project Description

Reduction of child mortality is reflected in several of the United Nations' Sustainable Development Goals and is a key indicator of human progress.The UN expects that by 2030, countries end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce under 5 mortality to at least as low as 25 per 1,000 live births. Parallel to the notion of child mortality is of course maternal mortality, which accounts for 295 000 deaths during and following pregnancy and childbirth (as of 2017). The vast majority of these deaths (94%) occurred in low-resource settings, and most could have been prevented.In light of what was mentioned above, Cardiotocograms (CTGs) are a simple and cost accessible option to assess fetal health, allowing healthcare professionals to take action in order to prevent child and maternal mortality. The equipment itself works by sending ultrasound pulses and reading its response, thus shedding light on fetal heart rate (FHR), fetal movements, uterine contractions and more.  In this project, we have some characteristics of Fetal Health as a dataset. The target variable of this dataset is Fetal Health. Since it is a multi class classification, the classes are represented by ‘Normal’, ‘Pathological’ and ‘Suspect’.

Technical Architecture::


Project Flow: 
  • User interacts with the UI to enter the input. 
  • Entered input is analyzed by the model which is integrated. 
  • Once model analyzes the input the prediction is showcased on the UI 
To accomplish this, we have to complete all the activities listed below
  • Define Problem / Problem Understanding 
    • Specify the business problem 
    • Business requirements 
    • Literature Survey 
    • Social or Business Impact. 
  • Data Collection & Preparation 
    • Collect the dataset 
    • Data Preparation
  • Exploratory Data Analysis 
    • Descriptive statistical 
    • Visual Analysis 
  • Model Building
    • Training the model in multiple algorithms 
    • Testing the model 
  • Performance Testing 
    • Testing model with multiple evaluation metrics
  • Model Deployment 
    • Save the best model 
    • Integrate with Web Framework

  • Project Demonstration & Documentation
    • Record explanation Video for project end to end solution
    • Project Documentation-Step by step project development procedure

Project Structure:

Create the Project folder which contains files as shown below:


  • We are building a flask application which needs HTML pages stored in the templates folder and a python script app.py for scripting. 
  • model.pkl is our saved model. Further we will use this model for flask integration. 
  • Training folder contains a model training file.



Project Activities

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