Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

Keyphrases

• Fetus 100%
• Machine Learning Approach 100%
• Fetal Distress 100%
• Fetal Hypoxia 100%
• Metabolic Acidosis 66%
• Clinical Data 66%
• Machine Learning Algorithms 66%
• Oxygen Deprivation 66%
• Disability 33%
• Most Common Cause 33%
• Serious Condition 33%
• PH Value 33%
• Cerebral Palsy 33%
• Classification Performance 33%
• Central Nervous System 33%
• Total Concentration 33%
• Area under the Curve 33%
• Cesarean Section (C-section) 33%
• Conventional Analysis 33%
• Partum 33%
• Newborn Infant 33%
• Alkalinity 33%
• Extracellular Volume 33%
• Fetal Well-being 33%
• Fetal Heart Rate 33%
• Machine Learning Classifiers 33%
• Confusion Matrix 33%
• Cause of Death 33%
• Selected Features 33%
• Classifier Model 33%
• Cardiotocography 33%
• Respiratory Acidosis 33%
• Blood Buffer 33%
• Hypoxic Effects 33%
• Labor Monitoring 33%
• Hydrogen Ion Concentration 33%
• Fetal Surveillance 33%
• Receiver Operating Characteristic Analysis 33%
• Obstetrics and Gynaecology 33%
• Acidity 33%
• Paper Machine 33%
• Respiratory Alkalosis 33%
• Base Deficit 33%
• Fluid Level 33%
• PhysioNet 33%

Medicine and Dentistry

• Metabolic Acidosis 100%
• Fetus Distress 100%
• Intrauterine Hypoxia 100%
• Base 50%
• Obstetrics 50%
• Spastic Diplegia 50%
• Central Nervous System 50%
• Cesarean Section 50%
• Buffer 50%
• Fetal Viability 50%
• Fetal Heart Rate 50%
• Interstitial Fluid 50%
• Extracellular Fluid 50%
• Respiratory Alkalosis 50%
• Fetal Surveillance 50%
• Gynecology 50%
• Respiratory Acidosis 50%
• Cardiotocography 50%
• Proton 50%

Biochemistry, Genetics and Molecular Biology

• Liquid 100%
• Fetal Well Being 100%
• pH 100%
• Alkalinity 100%
• Base Excess 100%
• Fetus Heart Rate 100%