Authors - Nagesh Sharma, Priyanka Yadav, Kavita Singh Abstract - An accurate determination of childhood malnutrition is necessary for preventive measures. This paper proposes a modified scoring scheme comprising two new elements: the Integrated Anthropometric Score (IAS) and the Hybrid Integrated Score (HIS). IAS uses six primary anthropometric measurements, such as BMI, MUAC, WHZ, WAZ, HAZ, and skinfold thickness, along with selected interaction terms that capture the non-linear connections between growth parameters. The weights are determined by regularized logistic regression, allowing the score to be transparent while still adapting to the statistical structure of the data set. To further stabilize the predictions, the HIS combines BAI, IAS, and a machine learning probability component to make the predictions robust in both synthetic and real-world samples. The models were developed using a synthetic dataset of 9,456 children and tested with five-fold cross-validation and a separate real-world dataset of 38 children. Interaction selection and regularization were performed to control noise sensitivity and avoid overfitting. The findings indicate that the IAS model outperforms BAI with its higher cross-validated accuracy (0.93) and strong performance on real data (0.95). The HIS stays consistent in accuracy across areas and indicates better generalization. The results suggest that by combining multidimensional anthropometric characteristics, interaction-aware modeling, and hybrid learning, a new, more adaptable, and clinically interpretable tool for predicting nutritional risk has been developed, surpassing traditional composite indices.
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