Browsing by Author "Gurudatha, S."

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    Beyond BMI: Machine Learning approach to identify superior obesity indicators for diabetes risk among Indian women
    (Springer, 2025) Gurudatha, S.; Majhi, R.
    Objective: To compare the effectiveness of five obesity indicators—body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHpR), and waist-corrected BMI (wBMI, given by wBMI = WC * BMI) for predicting type-2 diabetes (T2D) among women of reproductive age in India using machine learning (ML) methods. Methods: This cross-sectional study is based on the National Family Health Survey dataset of 2021. Data corresponding to 613,752 non-pregnant women were analyzed. Diabetes status was determined using World Health Organization criteria based on self-reports and random blood glucose levels. Six datasets were constructed, five using one obesity indicator each, and the sixth one using all five obesity indicators. The performances of tree-based classifiers (C5.0, Random Forest, XGBoost) and penalized regression models (Ridge, LASSO, ElasticNet) were compared. The models were evaluated based on metrics such as sensitivity, specificity, and Area under the Precision-Recall Curve (AUPRC), which is the recommended metric for imbalanced data. Results: Diabetes prevalence was 3.83%. The wBMI, WC, and WHtR were good predictors. The wBMI (median AUPRC 0.105, median sensitivity 63.6%) performed slightly better than the WC (median AUPRC 0.102, median sensitivity 62.33%) and the WHtR (median AUPRC 0.102, median sensitivity 62.7%). Among the ML models, the LASSO and Ridge classifiers outperformed tree-based techniques. Conclusion: The results highlight the importance of fat distribution in the body in diabetes diagnosis. The wBMI, WC, and the WHtR could be effective alternatives to the BMI to be used in diabetes screening programs for Indian women. The superior performance of penalized regression and the use of non-invasive predictors offer a scalable approach for diabetes screening in low-resource settings, supporting targeted public health interventions. © The Author(s), under exclusive licence to Research Society for Study of Diabetes in India 2025.
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    Robust Machine Learning Methods for Prediction of Childhood Anemia - A Case of the Empowered Action Group States of India
    (Institute of Electrical and Electronics Engineers Inc., 2024) Gurudatha, S.; Majhi, R.
    Anemia is a major undernutrition concern in developing countries. Anemia in early childhood leads to lower immunity and diminished cognitive development and is one of the major causes of early childhood mortality. In India, the major burden of anemia is seen in the Empowered Action Group (EAG) states. Concerted efforts are needed to reduce the burden of anemia. This study uses machine learning (ML) models to predict anemia among children aged six to fifty-nine months using data from the fifth round of the Indian Demographic and Health Survey (DHS), also known as the National Family Health Survey - 5 (NFHS - 5) in the EAG states. The dataset had 85,189 rows. The random oversampling method was used to balance the dataset as there was a class-imbalance issue. Four ML models, namely conditional inference (CI) tree, random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), were developed for prediction. The models were compared based on metrics such as accuracy, sensitivity, specificity, precision, and F1 score. The RF model had the best overall accuracy of 64%. The RF and XGB models had the best sensitivity of 0.75 and 0.7, respectively. The CI tree model had the highest specificity of 0.59. The RF and XGB models had the best F1 scores of 0.74 and 0.72, respectively. The RF model pointed out that the mother's nutritional status is the most important factor in predicting childhood anemia. Children were more likely to be anemic if their mothers had low Body Mass Index (BMI). This study contributes to the body of literature using ML techniques to study anemia in children. © 2024 IEEE.