Child Mortality Prediction Using Machine Learning Techniques

Abstract

Childmortalityremainsasignificantglobal health concern, particularly in developing nations. Predicting child mortality using machine learning techniques offers a promising approach to identifying at-risk children and enabling early interventions. This study explores various machine learning models to predict child mortality based on factors such as health conditions, socioeconomic status, environmental influences, anddemographic attributes.By utilizing historical and real-time datasets, preprocessing techniques, and feature

Introduction

Child mortality, defined as death before age five, remains a major global health issue, especially in low- and middle-income countries (LMICs). Despite medical advancements and public health improvements, millions of children die annually from preventable causes such as malnutrition, poor healthcare access, and infectious diseases.

Limitations of Traditional Methods

Traditional statistical models and demographic studies provide useful insights but often fail to capture the complex, non-linear interactions among multiple risk factors.

Role of Machine Learning

Machine Learning (ML) offers a data-driven, accurate, and scalable approach to predicting child mortality. Models like:

• Decision Trees

• Support Vector Machines (SVM)

• Artificial Neural Networks (ANN)

• Ensemble Models (e.g., Random Forest, XGBoost)

...can analyze diverse features such as maternal health, birth weight, immunization records, socio-economic status, and environmental conditions.

Proposed System

The proposed system follows a structured pipeline:

1. Data Collection: From WHO, UNICEF, hospitals, and national health databases.

2. Preprocessing: Cleaning data, handling missing values, encoding categorical variables, normalization, and outlier removal.

3. Feature Selection: Using RFE, PCA, and correlation analysis to identify key mortality factors.

4. Model Training & Evaluation: Applying and comparing multiple ML models using cross-validation and performance metrics (accuracy, precision, recall, F1-score, AUC-ROC).

5. Deployment: A user-friendly interface for real-time predictions integrated into EHR systems, providing risk scores and explanations for healthcare decision-making.

Ethical Considerations

The system incorporates:

• Bias mitigation techniques.

• Explainable AI (using SHAP, LIME) for model transparency.

• Data privacy and security standards (e.g., HIPAA, GDPR).

Research Findings

• Ensemble models (Random Forest, XGBoost) and deep learning outperform traditional statistical methods.

• Critical predictors: maternal health, immunization, socio-economic status, birth weight.

• Data quality and model interpretability are essential for real-world deployment.

Challenges & Future Work

• Data availability and completeness, especially in LMICs.

• Ethical concerns: fairness, transparency, and equity in predictions.

• Improved explainability and real-time data integration (e.g., wearables, IoT).

• Federated learning and global dataset expansion for scalable and privacy-preserving models.

Conclusion

Machine learning presents a powerful approach to predicting child mortality by analyzing complex patterns in health, socio-economic, and environmental data. The study demonstrates that advanced ML models, particularly ensemble techniques, offersuperioraccuracy inidentifyinghigh- risk children. The implementation of such predictive systems can aid healthcare providers and policymakers in designing targeted interventions to reduce child mortalityrates.However,ethicalchallenges suchasdatabias,transparency,andfairnessmust be addressed to ensure equitable healthcare outcomes. Future research should explore integrating real-time health monitoring systems and explainable AI techniques to enhance predictive accuracy and trustworthiness. By leveraging data- driven approaches, machine learning can contribute significantly to reducing child mortalityandimprovingglobalchildhealth outcomes

References

[1] Kumar, R., Singh, P., & Patel, V. (2020). Predicting infant mortality using machine learning techniques: A comparative analysis. International Journal of Medical Informatics, 138, 104117. [2] Gupta, A., & Sharma, S. (2019).Deeplearningapproaches forchild mortality prediction using maternal health data.IEEETransactionsonComputational BiologyandBioinformatics,17(5),1542-1553 [3] Rahman, M., Alam, T., &Hossain, M. (2021). Analyzing socio-economic determinants of child mortality using machine learning models. BMC Public Health, 21, 342. [4] Jones, D., Patel, R., & Lee, H. (2020). Integration of real-time health monitoring andmachinelearningforearlychildmortalityprediction.JournalofBiomedical Informatics, 107, 103765. [5] Ali, F., & Hassan, M. (2018). Machine learning approaches to predicting child mortality: A case study on historical health records.HealthInformaticsJournal,24(2), 145-160. [6] Smith, J., Roberts, K., & Wilson, D. (2022). Explainable AI for child mortality prediction:EnhancingtrustinhealthcareAI systems.ArtificialIntelligenceinMedicine, 127, 102302. [7] World Health Organization (WHO). (2021). Global health estimates: Child mortality trends and predictive modeling. WHO Report on Child Health Statistics. [8] UNICEF. (2020). Reducing child mortality through predictive analytics: A machine learning perspective. UNICEF Health Reports. [9] Bengtsson, S., & Mishra, P. (2019). The role of AI in reducing child mortality: A review of recent advancements. Journal of Global Health Research, 5(2), 101-118. [10] Verma, K., & Prasad, R. (2021). AI- driven healthcare solutions for child mortality prediction in developing nations. ComputationalandStructural Biotechnology Journal, 19, 5381-5392. [1] Kumar, R., Singh, P., & Patel, V. (2020). Predicting infant mortality using machine learning techniques: A comparative analysis. International Journal of Medical Informatics, 138, 104117. [2] Gupta, A., & Sharma, S. (2019).Deeplearningapproaches forchild mortality prediction using maternal health data.IEEETransactionsonComputational BiologyandBioinformatics,17(5),1542-1553 [3] Rahman, M., Alam, T., &Hossain, M. (2021). Analyzing socio-economic determinants of child mortality using machine learning models. BMC Public Health, 21, 342. [4] Jones, D., Patel, R., & Lee, H. (2020). Integration of real-time health monitoring andmachinelearningforearlychildmortalityprediction.JournalofBiomedical Informatics, 107, 103765. [5] Ali, F., & Hassan, M. (2018). Machine learning approaches to predicting child mortality: A case study on historical health records.HealthInformaticsJournal,24(2), 145-160. [6] Smith, J., Roberts, K., & Wilson, D. (2022). Explainable AI for child mortality prediction:EnhancingtrustinhealthcareAI systems.ArtificialIntelligenceinMedicine, 127, 102302. [7] World Health Organization (WHO). (2021). Global health estimates: Child mortality trends and predictive modeling. WHO Report on Child Health Statistics. [8] UNICEF. (2020). Reducing child mortality through predictive analytics: A machine learning perspective. UNICEF Health Reports. [9] Bengtsson, S., & Mishra, P. (2019). The role of AI in reducing child mortality: A review of recent advancements. Journal of Global Health Research, 5(2), 101-118. [10] Verma, K., & Prasad, R. (2021). AI- driven healthcare solutions for child mortality prediction in developing nations. ComputationalandStructural Biotechnology Journal, 19, 5381-5392.

Copyright

Copyright © 2025 V. Hanumantha Rao , Chelikam Tejaswini, Veeramreddy Swetha, Korrapati Mahammad Iliyaz, N. Sudarshan, S. Shakil Ahamad . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.