Neonatal Disease Prediction Using Machine Learning Techniques
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Published on: August 16, 2020
Yohanes Gutema Robi1, Tilahun Melak Sitote2
1Department of Information Systems, College of Computing and Informatics, Haromaya University, P.O. Box 138, Dire Dawa, Ethiopia.
Insights
Machine learning accurately predicts neonatal diseases like sepsis and respiratory distress syndrome. A stacking model achieved 97.04% accuracy, aiding early diagnosis in resource-limited settings.
Area of Science:
- Neonatal medicine
- Medical informatics
- Machine learning in healthcare
Background:
- Neonatal diseases are a major cause of global under-five mortality.
- Current diagnostic challenges include symptom similarity, delayed detection, and limited resources in countries like Ethiopia.
- Inadequate access to diagnostic tools and a shortage of neonatal health professionals contribute to misdiagnosis.
Purpose of the Study:
- To develop and evaluate a machine learning model for the early prediction and accurate diagnosis of common neonatal diseases.
- To address diagnostic limitations in resource-limited settings by leveraging historical patient data.
- To compare the performance of a classification stacking model against other machine learning algorithms.
Main Methods:
- A classification stacking model was developed using historical neonatal patient data from Asella Comprehensive Hospital (2018-2021).
- The model focused on four major neonatal diseases: sepsis, birth asphyxia, necrotizing enterocolitis (NEC), and respiratory distress syndrome.
- Performance was evaluated against XGBoost, Random Forest, and Support Vector Machine models.
Main Results:
- The developed stacking model achieved a high accuracy of 97.04%.
- The stacking model demonstrated superior performance compared to XGBoost, Random Forest, and Support Vector Machine.
- The findings indicate the potential for machine learning in improving neonatal disease diagnosis.
Conclusions:
- Machine learning, particularly the proposed stacking model, offers a promising solution for early and accurate diagnosis of neonatal diseases.
- This approach can significantly benefit resource-limited health facilities by overcoming diagnostic challenges.
- The study highlights the potential to reduce neonatal morbidity and mortality through advanced predictive analytics.
Abstract:
Neonatal diseases are among the main causes of morbidity and a significant contributor to underfive mortality in the world. There is an increase in understanding of the pathophysiology of the diseases and the implementation of different strategies to minimize their burden. However, improvements in outcomes are not adequate. Limited success is due to different factors, including the similarity of symptoms, which can lead to misdiagnosis, and the inability to detect early for timely intervention. In resource-limited countries like Ethiopia, the challenge is more severe. Low access to diagnosis and treatment due to the inadequacy of neonatal health professionals is one of the shortcomings. Due to the shortage of medical facilities, many neonatal health professionals are forced to decide the type of disease only based on interviews. They may not have a complete picture of all variables that have a contributing effect on neonatal disease from the interview. This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia, necrotizing enter colitis (NEC), and respiratory distress syndrome. These diseases account for 75% of neonatal deaths. The dataset has been obtained from the Asella Comprehensive Hospital. It has been collected between 2018 and 2021. The developed stacking model was compared to three related machine-learning models XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). The proposed stacking model outperformed the other models, with an accuracy of 97.04%. We believe that this will contribute to the early detection and accurate diagnosis of neonatal diseases, especially for resource-limited health facilities.
