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An idiosyncratic MIMBO-NBRF based automated system for child birth mode prediction
1Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600 119, Tamilnadu, India.
Artificial Intelligence in Medicine
|September 6, 2023
Summary
This study introduces a novel machine learning system for predicting child birth mode. The proposed MIMBO-NBRF hybrid model achieves high accuracy, improving upon existing methods.
Area of Science:
- Medical Informatics
- Computational Biology
- Machine Learning
Background:
- Predicting child birth mode remains a complex challenge with limited conventional methodologies.
- Existing approaches lack robust techniques for accurate birth mode prediction.
Purpose of the Study:
- To develop a novel optimization-based machine learning technique for a child birth mode prediction system.
- To create an efficient automated system using a hybrid machine learning model.
Main Methods:
- Implemented data imputation for dataset quality enhancement.
- Utilized Multivariate Intensified Mine Blast Optimization (MIMBO) for feature selection.
- Developed an integrated Naïve Bayes - Random Forest (NBRF) classifier, enhanced with Bird Mating (BM) optimization for likelihood parameter estimation.
Main Results:
- The MIMBO-NBRF technique significantly improved classifier performance.
- Achieved low computational burden and increased prediction accuracy.
- Demonstrated superior results compared to existing methods, reaching an average accuracy of 99%.
Conclusions:
- The proposed MIMBO-NBRF framework offers a simple yet efficient automated system for child birth mode prediction.
- The hybrid model effectively integrates advanced optimization and classification techniques.
- The system shows significant improvements in prediction accuracy and computational efficiency.
