Performance Comparison of Three Classifiers for Fetal Health Classification Based on Cardiotocographic Data
| Autor: | Vijay Khare, Sakshi Kumari |
|---|---|
| Rok vydání: | 2022 |
| Zdroj: | Acadlore Transactions on AI and Machine Learning. :52-60 |
| ISSN: | 2957-9570 2957-9562 |
| DOI: | 10.56578/ataiml010107 |
| Popis: | The global child mortality rate, which is steadily declining, will be around 26 fatalities per 1000 live births in 2022. Numerous Sustainable Development Goals of the United Nations take into account the declining child mortality rate, which illustrates how far humanity has come. Cardiotocograms (CTGs) are a simple and affordable tool that most professionals choose to reduce infant and mother mortality. Three of the most cutting-edge methodologies are utilized in this research to classify the data, and their results are compared. All three classifiers outperformed the random forest, whose accuracy was 94.3%. |
| Databáze: | OpenAIRE |
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