Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach

Engineering & Materials Science

• Pediatrics 100%
• Platelets 94%
• Hemoglobin 69%
• Blood 67%
• Machine learning 53%
• Random forests 51%
• Cells 47%
• Learning algorithms 40%
• Biochemistry 35%
• Potable water 30%
• Decision trees 23%
• Classifiers 17%

Medicine & Life Sciences

• Machine Learning 83%
• Precursor Cell Lymphoblastic Leukemia-Lymphoma 72%
• Pediatrics 47%
• Consanguinity 17%
• Forests 15%
• Child 14%
• Hemoglobins 10%
• Supervised Machine Learning 10%
• Leukocytes 10%
• Datasets 10%
• Blood Platelets 10%
• Decision Trees 8%
• Ecosystem 7%
• Drinking Water 6%
• Platelet Count 6%
• Social Class 6%
• Biochemistry 6%