A machine learning-based clinical decision support system for effective stratification of gestational diabetes mellitus and management through Ayurveda.

BACKGROUND: Gestational Diabetes Mellitus (GDM) is a metabolic condition that develops in course of pregnancy. The World Health Organization describes it as carbohydrate intolerance that causes hyperglycemia of varying severity and manifests itself or is first noticed during pregnancy. Early prediction is now possible, owing to the application of cutting-edge methods like machine learning.

OBJECTIVE: In the proposed empirical study, different machine-learning algorithms are applied to predict the prospective risk factors influencing the progression of GDM in gestating mothers.

MATERIALS AND METHODS: The performance of these algorithms is evaluated through accuracy, precision, f1-score, etc. The lifestyle interventions and medications listed in Ayurveda literature are discussed for effective management of the disease.

RESULTS: Most of the proposed classifiers achieved a reasonable accuracy range of 75-82 %. Appropriate lifestyle changes, herbal remedies, decoctions, and churnas have all been shown to be useful in lowering the risk of GDM. Early detection using machine learning models can significantly reduce disease severity by facilitating timely Ayurvedic interventions.

CONCLUSION: The proposed work is more focused on the identification of factors impacting GDM in expectant women. A balanced diet with physical exercise, proper medication, and better lifestyle management (through Garbini Paricharya) can control the perils of GDM if diagnosed prematurely.