Healthcare
AutoML for NAFLD Screening Diagnosis Using MLJAR: Zero-Code Machine Learning Models Based on Anthropometric Measures
- AutoML in healthcare
- machine learning NAFLD
- AI liver disease screening
- MLJAR AutoML research
- zero-code machine learning
- anthropometric prediction models
- automated model selection medicine
- AUC ROC medical AI
- low cost AI screening
- machine learning BMI waist ratio
MLJAR tools were used in the following publication.
Leveraging AutoML to provide NAFLD screening diagnosis: Proposed machine learning models
Ali Haider Bangash
Shifa College of Medicine, Shifa Tameer e Millat University, Islamabad, Pakistan
This medRxiv preprint presents a healthcare application of Automated Machine Learning (AutoML) using the MLJAR platform for non-alcoholic fatty liver disease (NAFLD) screening. The study developed multiple machine learning models using only easy-to-measure anthropometric variables such as BMI and waist-to-hip ratio. The models achieved good discriminative performance (AUC up to 0.826) using 15-fold cross-validation on a cohort of 4,053 subjects. The work demonstrates how zero-code AutoML can enable cost-effective medical screening solutions without requiring programming expertise.
medRxiv • October 22, 2020
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