Table of Contents

• Introduction
• Objective
• Dataset
• Data pre-processing
• Installation
• Usage
• Models
• Results
• Model Interpretation
• Ethical Considerations
• Acknowledgements

Introduction

Maternity Care Modeling is an interdisciplinary field that combines healthcare, data science, and artificial intelligence to enhance the understanding and prediction of various aspects of pregnancy and childbirth. This includes predicting pregnancy outcomes, such as complications or the likelihood of a successful pregnancy, as well as predicting the mode of delivery, whether it's vaginal or through a cesarean section (C-section).

Significance

Pregnancy and childbirth are among the most transformative and crucial experiences in an individual's life. Maternity care plays a pivotal role in ensuring the well-being of both the expectant parent and the unborn child. By leveraging advanced data analysis and predictive modeling, we can:

• Improve Healthcare Decision-Making: Healthcare professionals can benefit from accurate predictions to make informed decisions about prenatal care, interventions, and delivery methods.
• Enhance Patient Care: Pregnant individuals can receive personalized care plans based on their unique risks and characteristics, leading to improved health outcomes and reduced maternal and neonatal mortality rates.
• Resource Allocation: Hospitals and healthcare systems can allocate resources more efficiently by identifying high-risk pregnancies in advance, reducing the burden on overtaxed healthcare facilities.

Objective

The main objectives of this project are as follows:

• Predict pregnancy outcomes (e.g., complications) using machine learning and deep learning techniques.
• Predict the mode of delivery (vaginal or cesarean section) for pregnant patients.
• Predicting the Approx. date of delivery
• Predicting the childbirth weight
• Predicting preeclampsia

We believe that Maternity Care Modeling has the potential to make a significant positive impact on the lives of expectant parents and their children.

Dataset

Data Description including information such as the data source, size, and a brief overview of the features.

Dataset/Resource Description	URL
Babies Birth Weight Dataset (Kaggle)	Link
Fetal Health Classification Dataset (Kaggle)	Link
UNICEF Country Data for India	Link
UNICEF Global Data Explorer for India	Link
World Bank Maternal Mortality Rate Indicator	Link
Maternal Health Risk Data (Kaggle)	Link
Our World in Data - Maternal Mortality	Link
Percentage Women Complication Pregnancy/Delivery Data (India)	Link
Pregnancy-Associated Mortality Data (New York City)	Link
CDC Wonder - Pregnancy Data (United States)	Link
Reproductive Child Healthcare Classification Dataset (Kaggle)	Link
UNICEF Healthy Mothers, Healthy Babies Resource	Link
Smoking During Pregnancy Statistics (England)	Link
Zenodo - Reproductive Healthcare Data	Link

Data Preprocessing

• Feature Engineering: Identifying the relevant features and extracting relevant features from the dataset that might contribute to better predictions.
• Data Splitting: Splitting the dataset into training, validation, and test sets is crucial for evaluating the model's performance.
• Normalization/Scaling: Normalizing and scaling the numerical features to ensure that they have the same range.

Models Selection and Architecture

List and briefly describe the machine learning and deep learning models you used in your project. Include any special considerations or hyperparameters.

• Logistic Regression/Random Forest/SVM
• Regression Models
• Neural Networks (CNN or RNN)
• Predictive Modeling

Model Workflow

Model Training

• Training separate models for each prediction task :
  • pregnancy outcomes
  • mode of delivery
  • approx. date of delivery
  • preeclampsia
  • childbirth weight

Optimizing the hyperparameters using the validation set

• Ensemble Methods
  • Stacking
  • Bagging

Used to combine predictions from multiple models for improved accuracy

Installation

Instructions on how to set up the project locally. Including any dependencies or libraries that need to be installed.

pip install -r requirements.txt

Results

• Evaluation Metrics Performance
  • Accuracy, Precision, Recall, F1-score, MAE, MSE
• Interpretability
  • SHAP values, feature importance scores, and gradient-based methods to interpret model predictions, especially for healthcare applications where interpretability is crucial.

Model Interpretation

Ethical Considerations

Acknowledgements

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