UttaraRisk-Next: A Multi-Task Ensemble Learning Framework for Maternal Health Risk Prediction

Abstract

In India's mountainous areas, maternal mortality is still a serious public health concern, especially in Uttarakhand, where access to healthcare is hampered by geographical obstacles. UttaraRisk-Next, a multi-task ensemble learning framework for thorough maternal health risk assessment, is presented in this paper. Three crucial outcomes are simultaneously predicted by the model: the probability of abortion, the continuous risk percentage (0–100%), and the risk of maternal mortality. We created 78 clinical features in accordance with WHO guidelines using a synthetic but epidemiologically representative dataset of 2,500 pregnancies from 13 districts in Uttarakhand. These features included blood pressure classifications, hemoglobin categories, and socioeconomic vulnerability indicators. UttaraRisk-Next employs an ensemble architecture combining gradient boosting and random forest models with isotonic calibration for probability refinement. On validation data (n=500), the model achieved: risk prediction MAE 5.557% with R^2=0.708 and 97.6% interval coverage; abortion classification ROC-AUC 0.558 with excellent calibration (ECE=0.020); mortality prediction ECE=0.001 despite rare event frequency (0.6%). Comprehensive fairness analysis across rural-urban, age, and socioeconomic dimensions demonstrated equitable performance (ECE differences <0.025). The model identifies 22.4% of pregnancies as high-risk, enabling targeted resource allocation. With 2.1ms inference time and 45MB memory footprint, UttaraRisk-Next is deployable in resource-constrained settings, directly supporting SDG-3.1 (maternal mortality reduction) and SDG-5 (gender equality) objectives in the Indian Himalayan region