Using Neural Networks with Different Target Settings to Predict Maternal Health Risks
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Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105034669517
Journal Title
2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025
Start Page
2284
End Page
2288
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SCOPUS
Bibliographic Citation
2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025 (2025) , 2284-2288
Suggested Citation
Meesri S., Amornsamankul S., Kraipeerapun P. Using Neural Networks with Different Target Settings to Predict Maternal Health Risks. 2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025 (2025) , 2284-2288. 2288. doi:10.1109/CICN67655.2025.11368035 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116088
Title
Using Neural Networks with Different Target Settings to Predict Maternal Health Risks
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Abstract
This paper proposes a technique for training neural networks with multiple outputs using different target settings. Three neural networks with the same configuration are set up with different targets. They are trained using the same data to predict different outputs, which are then combined to produce the final result. Maternal health risk dataset from the UC Irvine machine learning repository is used to test the proposed technique. This technique can achieve better accuracy than an ensemble neural network and stacking neural network trained with the original targets. In addition, the proposed technique can give better accuracy when combined with cascade generalization.