Prediction of Childbirth Outcomes Using XGBoost, Data‑Driven

Insights and Evaluation

M. Hariomprakaas, V. Vennila, V. Sharmila and S. Savitha

Department of Computer Science and Engineering, K.S.R. College of Engineering, Tiruchengode - 637215, Tamil Nadu,

India

Keywords: Childbirth Outcome Prediction, Synthetic Medical Data, XGBoost Classifier, SMOTE for Imbalanced Data,

Maternal Healthcare Analytics.

Abstract: Predicting the childbirth outcome is an important problem in maternal healthcare since it effectively

minimizes risks and helps decide an appropriate delivery method. Fueled by these developments in data-

science as well as to our knowledge there exist limited predictive models for delivery outcomes, this study

presents a solid framework which makes use of machine learning, synthetic data generation and novel

preprocessing techniques for prediction of delivery outcomes coded by expert. We addressed the challenge of

limited and imbalanced data frequently encountered in medical research by utilizing a robust synthetic data

with realistic range of maternal and fetal health parameters. We propose a framework based on the tuned

XGBoost classifier, which has both the accuracy and generalizability to meet this challenge, along with a

regularized objective function that leads to a solution minimizing predictive performance at the expense of

model complexity. Dealing with various data types and missing values, we pre-processed the data using a

simple yet powerful pipeline that handles these problems implicitly, and as was previously noted, with the

use_SMOTE argument switched to True, deployments are ensured the balancing of classes, as well the

sampling of high-risk outputs. We thoroughly assess the model, employ cross-validation and stratified

sampling to demonstrate that it is accurate and interpretable. The current study has examined an approach

which can scale, but is also transparently operationalized within clinical workstreams, marking progress

toward enhanced maternal care outcome.

1 INTRODUCTION

Prediction of childbirth outcomes is crucial in

maternal healthcare, with the goal to reduce

complications and improve maternal and neonatal

health. Some effective prediction models can enable

healthcare personnel to take informed decisions,

improving the quality of care as well as minimising

delivery-associated risks. Conventional approaches

typically depend on clinical experience or basic

statistical models, potentially lacking the required

nuance and flexibility to accommodate complex

clinical interfaces. Machine learning (ML), the next

big thing, is an efficient alternative that uses data-

driven approaches to provide better prediction

accuracy and scalability.

With respect to ML techniques, ensemble learning

approaches such as XGBoost have recently

demonstrated themselves to be promising candidates

for classification tasks, primarily due to their shelf-

availability, robustness, and strong in-field

performance on structured data (T. Chen et al., 2016

and J. Friedman, 2001). A gradient boosting

algorithm, XGBoost, iteratively refines weak learners

to enhance prediction accuracy, tackling both

overfitting and underfitting (L. Breiman, 2001). It

incorporates advanced regularization for

generalizability at relatively little computational cost

compared to other classical algorithms such as

random forests (Y. Bengio, 2011).

Class imbalance is one of the main issues in all

medical datasets. In some cases, cesarean deliveries

act as a minority class as compared to vaginal

deliveries. Synthetic data generation techniques such

as SMOTE have been shown to help with this by

augmenting minority class instances (N. Chawla et

al., 2002). Which allows for training predictive

models on more balanced datasets, decreasing bias

and increasing confidence in predictions.

Hariomprakaas, M., Vennila, V., Sharmila, V. and Savitha, S.

Prediction of Childbirth Outcomes Using XGBoost, Data-Driven Insights and Evaluation.

DOI: 10.5220/0013931500004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

467-472

ISBN: 978-989-758-777-1

467

Pre-processing is also a very important part of any

data-driven pipeline, especially for medical

applications where datasets typically have numerical

and categorical features and are subject to be

incomplete. Proper pre-processing techniques play an

important role to maximize the performance of the

model for numerical data you have to use imputation

and scale the features S. Patel and H. Patel (2013) and

do one hot encoding for categorical variables.

Furthermore, sophisticated feature engineering

techniques like normalization and encoding are

employed to facilitate accurate detection of data

trends by ML algorithms (I. Guyon and A. Elisseeff,

2006).

A major innovation in this study is the use of

synthetic datasets. The methodology generates

realistic distributions of maternal and fetal health

parameters including maternal age, BMI, blood

pressure, and fetal heart rate, overcoming the

challenge of limited availability of medical data due

to privacy concerns P. Domingos (2012). The

parameters used are pertinent predictors of childbirth

outcome, and their inclusion serves to realize the

multifactorial aspect of pregnancy risk (F. Pedregosa

et al., 2011). The inclusion of both continuous and

categorical features makes the model more widely

applicable across different clinical settings.

Moreover, the implementation of XGBoost

combined with hyperparameter tuning offers a

powerful prediction framework.” It minimizes a

regularized objective function that balances model

complexity and accuracy, a key requirement in the

case of any clinical application (T. Chen et al., 2016

and G. Shapley, 1953). The inclusion of cross-

validation and stratified sampling also contributes to

the model's reliability by ensuring that its predictions

generalize effectively to previously unencountered

data.

In this study, we describe an overall workflow for

predicting childbirth complications based on

synthetic medical data, a pre-processing pipeline, and

XGBoost based ML model. It deals with problems

such as data imbalance, feature variability and limited

accessibility to real-world datasets. The proposed

approach focuses on providing a clinical decision-

support system that can utilize both the deep learning

model and evaluation methods to provide scalability

and interpretable analytics in maternal healthcare

settings, while observing defined optimal stopping

criteria.

2 RELATED WORKS

In recent years, prediction of medical outcomes,

especially in maternal care, has received considerable

attention, as machine learning (ML) has the potential

to transform clinical care decision-making. This has

led to a diversity of theories suggested to explain this

phenomenon, from classical statistical models to

more contemporary ML methods which each have

their strengths and weaknesses.

The groundwork for some of this work was laid

early on by explorations primarily through rule-based

systems and statistical models, where rules were built

from ground up. ML was born and became one of the

most powerful predictive technology that can find

complex patterns in data. Ensemble methods based on

trees, such as random forests (Y. Bengio, 2011) and

gradient boosting machines (T. Chen et al., 2016 and

L. Breiman, 2001), have shown to work better on

structured healthcare datasets.

ML has also been used to enhance prediction

precision in recent enhanced devices in medical

imaging, medical diagnostics, and laboratory

planning. For instance, Kagadis et al. ML techniques

like the ones described are proving useful for the

automation of prenatal care, in areas like the analysis

of fetal ultrasound imaging for abnormalities and "big

data" to stratify high risk pregnancies. Although these

studies were to a large extent for imaging data, the

insights are aligned very well with our work which

focuses on tabular clinical data with respect to feature

importance and interpretability.

One of the most remarkable advancements in

healthcare analytics is the convergence of synthetic

data generation with preprocessing pipelines. Dee and

Hogg studied neonatal outcomes prediction

highlighting the need for dealing with data scarcity

using synthetic data. Similarly, Silva et al. As an

example, showed that ML models could be useful in

the prediction of complications during pregnancy,

showcasing the importance of preprocessing to

ensure data quality. Synthetic data generation is

essential due to the hurdles associated with fine-

tuning existing models based on the insufficient real-

world datasets, aligning with the methods of these

studies.

Moreover, interpretability is an essential feature

of ML models in healthcare as well. We have also

seen the emergence of Shapley value-based

interpretability methods that allow clinicians to

understand the rationale behind model predictions.

This underscores the focus of our work on providing

actionable insights via feature importance analyses

and ensuring that the model outputs are not only

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

468

accurate but also provide transparency to

practitioners.

Lastly, LeCun et al. recognized the transformative

potential of deep learning in healthcare, especially in

high-dimensional data settings. Deep learning is often

touted as the go-to choose for image and unstructured

data, but this work proves that simpler models such

as XGBoost can match its performance as long as

proper pre-processing techniques to balance the

datasets are applied.

Thus, although many existing works provide a

great contribution to predictive analytics for maternal

healthcare, our study extends the field to include

synthetic data generation, relevance feature

extraction and a tuned version of XGBoost model

which can be utilised to take care of real problems like

data imbalance and unavailability of real-world data.

Drawing together several insights from the related

works, we propose a scalable and interpretable

framework specifically designed to predict childbirth

outcomes.

3 PROBLEM STATEMENT

Despite its importance for the prevention of maternal

and neonatal morbidity, there is an undeniable

heterogeneity of risk factors that governs delivery

(e.g. maternal age, comorbidities, clinical, surgical

history). Standard methods practice subjective

scorecards or limited statistics-based modeling,

which often underestimate risk as these approaches

may not consider a wide range of factors–for example

high maternal BMI, mal-presentation of the fetus,

diabetes or hypertension, to name only a few.

Additionally, the limited number of complete,

labelled medical datasets available and the

commonality of imbalanced data both contribute to

the challenge of developing robust predictive models.

Existing tools are limited to historical hospitalisation

data, which is not a predictor of risk (during case

surges) and does not capture the heterogeneous

outcome of mild cases. Therefore, there is a growing

necessity for a scalable, interpretable, and data-driven

framework that captures the relevant correlation

between synthetic and real-world data to predict

delivery outcomes and guide clinical management. In

this study, the authors aimed to bridge this gap

through the integration of state-of-the-art machine

learning approaches like XGBoost along with a well-

defined data pre-processing and evaluation pipeline

to provide a pragmatic approach to enhance maternal

healthcare.

4 METHODOLOGY

The proposed methodology leverages advanced

machine learning techniques to predict childbirth

outcomes, focusing on the integration of synthetic

medical data generation, preprocessing pipelines, and

a robust classification model. The overall approach is

modular, ensuring clarity, reproducibility, and

extensibility. Below, we outline the steps in detail:

4.1 Synthetic Data Generation

Given the limitation on the availability of real-world

data, we created a synthetic dataset that simulates

clinical parameters that are well established to be

associated with pregnancy. This dataset reflects

realistic distributions and variability present in

maternal and fetal health metrics. Important features

are maternal age, BMI, blood pressure, blood sugar,

hemoglobin, fetal heart, etc. These structured features

were combined with other categorical variables

including fetal position, previous cesarean history,

presence of conditions such as diabetes and

hypertension. Using the constructed medical

conditions, we synthesized a binary target variable

(delivery_mode) which indicated if the delivery

would likely be vaginal (0) or cesarean (1).

4.2 Preprocessing Pipeline

Since we have mixed types of features (numerical and

categorical), we devised a preprocessing pipeline to

standardize and encode the data.

• Numerical Features: for these types of features,

missing values were filled by the median value

from the same feature, and then, normalization

was applied (standard scaling). This also

ensured that all the features were on the same

scale followed by model’s performance

optimization.

• Categorical Features: We imputed missing

categorical features with a constant value

("missing") and one-hot encoded the features.

Thus, the machine learning model was able to

handle categorical variables.

4.3 Handling Class Imbalance

Class imbalance is a standard problem in medical

datasets in which negative events are comparably

rare. In, to overcome this, the Synthetic Minority

Over-sampling Technique (SMOTE) was used to

balance the target variable distribution. This approach

Prediction of Childbirth Outcomes Using XGBoost, Data-Driven Insights and Evaluation

469

creates synthetic examples for the underrepresented

class, ensuring that the model is educated on a fair

dataset and reducing type toward the dominant class.

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4.4 Model Development

The classification model was implemented using the

eXtreme Gradient Boosting (XGBoost) algorithm,

renowned for its scalability, accuracy, and robustness.

The model was fine-tuned with hyperparameters

optimized for the task:

• Number of Trees: 200

• Maximum Tree Depth: 6

• Learning Rate: 0.01

• Subsampling and Column Sampling: Set at

80% to prevent overfitting

• Minimum Child Weight: 3 for better control of

model complexity.

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4.5 Model Training and Evaluation

The dataset was divided into training and testing sets

(80:20 ratio), using stratified sampling to preserve the

distribution of the target variable. The next steps were

done:

• Cross-validation: These models were assessed

with 5-fold stratified cross-validation on the

training data to estimate their generalization

performance. Mean accuracy and standard

deviation were recorded as metrics.

• Model Evaluation: The trained model was

validated on the test set using metrics such as

accuracy, ROC-AUC, and a comprehensive

classification report (precision, recall, F1-

score). These metrics were a good

representation of high-level performance on

embedding classification and how well the

model learned to separate classes.

4.6 Model Deployment and Prediction

After successful training, the model was tested on

unseen patient data with similar feature distributions.

The preprocessing pipeline ensured that the new data

was transformed in alignment with the training data.

Predictions included:

• Binary Classification: Indicating vaginal or

cesarean delivery.

• Probability Scores: Providing the confidence

level of the predictions.

4.7 Model Saving and Version Control

To facilitate reproducibility and scalability, the

trained model, along with the preprocessing pipeline,

evaluation metrics, and feature metadata, was

serialized and saved using the joblib library.

Versioning was incorporated to track model

iterations, ensuring transparency in updates.

5 RESULT AND DISCUSSION

An artificial dataset mimicking real-world maternal

and fetal health parameters was used to evaluate the

proposed methodology for predicting childbirth

outcomes. This model performed remarkably well

with a test accuracy of 96.5%, and a mean cross-

validation accuracy (±SD) of 96.8% (±0.2), while the

ROC-AUC score for the model was calculated at

96.9%, showing the model's ability to distinguish

between vaginal and cesarean deliveries. (This is a

good demonstration of how accurate the XGboost

classifier is, especially for imbalanced dataset, and

how much meaningful features it can learn from the

data.)

One of the things that was very important was to

incorporate a preprocessing pipeline which

significantly improved the model. This allowed the

model to make use of all types of data by

standardizing numerical features and one hot

encoding categorical variables. The table 1 shows the

Evaluation Metrics Since the dataset was imbalanced,

this technique ensured that our model was relatively

sensitive to predicting the minority class. This is

especially relevant in clinical context where precise

recognition of high-risk births (cesarean birth

outcomes) is paramount.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

470

Table 1: Evaluation metrics.

Metric Value

Test Accuracy 96.5%

Cross-Validation Mean

Accurac

96.8% ± 0.2

ROC-AUC Score 96.9%

Precision (Cesarean) 97.0%

Recall (Cesarean) 94.0%

F1-Score (Cesarean) 95.4%

Precision (Normal delivery) 96%

Recall (Normal delivery) 98%

F1-Score 97%

5.1 Future Enhancements

In the future, we aim at applying the proposed

methodology on real life clinical datasets to make

them robust to generalization and to most importantly

include additional maternal and fetal health

parameters to the feature set. Improving performance

and interpretability can be achieved by incorporating

advanced deep learning techniques for complex data,

as well as hybrid models that integrate machine

learning with domain-specific knowledge.

Deployment into real-time systems, like EHRs,

would enable prediction during a clinical visit, and

natural language processing could extract knowledge

from free-text data. Addressing ethical considerations

such as data privacy and bias, will lead to equitable

and responsible application. These advances will

convert the framework into a holistic decision-

support system that can elevate mother and neonate

care around the world.

6 CONCLUSIONS

In this work, we propose a novel end-to-end

framework comprising synthetic data generation,

advanced preprocessing and a robust model for

childbirth outcome prediction. The presented strategy

classifies delivery results as vaginal or cesarean and

shows remarkable performance and reliability while

tackling such key issues, such as data scarcity, class

imbalance, and feature variability. Scalability and

adaptability are ensured by integrating and

optimizing XGBoost using rigorous evaluation

metrics and clinical applicability is enhanced by

interpreting the model through feature importance

analysis. These findings validate the framework's

potential role as a real-world clinical decision support

tool enabling healthcare decision makers to make

complex, informed, and risk-based decisions to

prioritize high-risk individuals for treatment. The

figure 1 shows the Probability of Cesarean Delivery.

Future development will add real data sets, real time

systems and ethical 'locks' making this a potential

transformative tool for maternal care adding the

benefits of data led approaches to routine clinical

practice. The figure 2 shows the Probability of

Normal Delivery.

Figure 1: Probability of cesarean delivery.

Figure 2 Probability of normal delivery.

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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