Machine Learning for Managing Gestational Diabetes with Ayurveda
Author Information
Author(s): Shetty Nisha P., Shetty Jayashree, Hegde Veeraj, Dharne Sneha Dattatray, Kv Mamtha
Primary Institution: Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
Hypothesis
How effectively can machine learning models identify the most influential features for predicting Gestational Diabetes Mellitus (GDM) during early gestation, and which machine learning algorithm performs best in classifying GDM among gestating mothers?
Conclusion
Early detection using machine learning models can significantly reduce disease severity by facilitating timely Ayurvedic interventions.
Supporting Evidence
- Most classifiers achieved an accuracy range of 75-82%.
- Appropriate lifestyle changes and Ayurvedic remedies can lower the risk of GDM.
- Machine learning can help identify high-risk mothers early for better management.
Takeaway
This study uses computer programs to help doctors find out which pregnant women might get diabetes, so they can help them stay healthy with Ayurvedic treatments.
Methodology
Different machine-learning algorithms were applied to predict risk factors influencing GDM, evaluated through accuracy, precision, and F1-score.
Potential Biases
Potential biases due to the dataset being collected from a single source and lack of external validation.
Limitations
The dataset's geographical specificity is unclear, and no real patients were used to test the method.
Participant Demographics
Pregnant women at risk of gestational diabetes.
Digital Object Identifier (DOI)
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