Fetal Risk Prediction Using Optimized Genetic Algorithm – Support Vector Machine Based Feature Selection Techniques
Paper Details
| Subject: | Computer Science and Engineering |
| Paper ID: | UIJRTV2I120014 |
| Volume: | 02 |
| Issue: | 12 |
| Pages: | 108-113 |
| Date: | October 2021 |
| ISSN: | 2582-6832 |
| Statistics: | |
Cite this
J. Jayashree, J. Vijayashree, and N.Ch.S.N. Iyengar, 2021. Fetal Risk Prediction Using Optimized Genetic Algorithm - Support Vector Machine Based Feature Selection Techniques. United International Journal for Research & Technology (UIJRT), 2(12), pp.108-113.
Abstract
Improved feature selection methodology for fetal risk data collection defining important features. The aim is to improve the fetal risk prediction rate by using an optimized technique such as GA-SVM for feature selection. Then the selected features are given to various classifiers such as random forest, naïve bayes, multi- layer perceptron and support vector machine for prediction. As a result, the feature selected by optimized feature selection techniques provides higher accuracy, precision and recall when compared to non-optimized techniques.
Keywords: Fetal, optimization, features, prediction.
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