Predictive Analysis of common risk factors in Neonates using Machine Learning
Main Article Content
Abstract
To identify the clinical–demographic risk factors contributing to birth asphyxia ongoing pregnancies and to suggest preventive measures. The proposed methodology is used to identify the risk factor associated with birth asphyxia such as- maternal risk factors, type of deliveries, educational status of mothers, the age distribution of mothers, gestation period, birth weight, etc. A hospital-based prospective observational study was carried out on 220 participants. After obtaining the consent of either of the parents, the detailed maternal data and history were taken in a structured questionnaire designed for the study purpose. The statistical evaluation of model performance is evaluated using these standard metrics.In this research, three types of machine learning classification algorithms which are Two class Neural networks, Two-Class Boosted Decision Tree, Multi-Class Decision Jungle were used to build the Birth asphyxia model to identify the overall risk factors associated with birth asphyxia. The findings showed that the Two-Class Boosted Decision Tree achieved the highest accuracy at 94.5%
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
