Abstract
This research explores machine learning approaches to determine the most significant features related to neonatal mortality in Indonesia. We create prediction tasks with deep learning models including MLP, LSTM, and CNN. We found that low birth weight and early breastfeeding becomes the most significant features related to neonatal mortality in Indonesia. For prediction task, implementing feature importance task as feature selection can improve prediction performance and reduce algorithm complexity. LSTM and CNN achieved the best prediction model with 90.91% accuracy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024 |
| Editors | Shoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 342-344 |
| Number of pages | 3 |
| ISBN (Electronic) | 9798350394924 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 10th International Conference on Applied System Innovation, ICASI 2024 - Kyoto, Japan Duration: 17 Apr 2024 → 21 Apr 2024 |
Publication series
| Name | Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024 |
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Conference
| Conference | 10th International Conference on Applied System Innovation, ICASI 2024 |
|---|---|
| Country/Territory | Japan |
| City | Kyoto |
| Period | 17/04/24 → 21/04/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- deep learning
- feature importance
- neonatal mortality
- prediction model
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Signal Processing
- Information Systems and Management
- Safety, Risk, Reliability and Quality
- Instrumentation
