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<p>Predicting unwanted pregnancies accurately may reduce abortion and population growth in a country. Almost half of India’s 48.1&#xa0;million pregnancies were unintended (Lancet Global Health, 2018). This paper uses National Family Health Survey (NFHS-4, 2015-16) data to explore the prediction of unwanted pregnancy using various statistical and machine learning models. It uses under-sampling to improve the predictive power of the models, given the imbalanced distribution of unwanted pregnancy rates in the data. We have proposed a weighted variable to eliminate the effect of under sampling. Among the machine learning approaches tested, Random Forest stands out as the strongest for predicting unwanted pregnancies, delivering 80.35% accuracy and an AUC score of 0.86 on the test data. The model outputs suggest that unmet need for contraception, knowledge of contraception methods, age at first birth, wealth status, total children ever born, knowledge of ovulation cycle, place of residence, woman’s education, woman’s age, and marital duration are the strongest predictors to estimate the unwanted pregnancy. The trained model has also been validated and performed well on recent NFHS-5 data (2019-21). The model can be deployed by policymakers in the sub-regions of India in the future to predict the prevalence of unwanted pregnancy and run a customized campaign to reduce the events of unintended pregnancy through various medical and social interventions.</p>

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A Machine Learning Modeling Approach to Predict Unwanted Pregnancy in India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月16日

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  • Prashant Verma,
  • Mukti Khetan,
  • Kaushalendra Kumar Singh,
  • Ujjaval Srivastava

摘要

Predicting unwanted pregnancies accurately may reduce abortion and population growth in a country. Almost half of India’s 48.1 million pregnancies were unintended (Lancet Global Health, 2018). This paper uses National Family Health Survey (NFHS-4, 2015-16) data to explore the prediction of unwanted pregnancy using various statistical and machine learning models. It uses under-sampling to improve the predictive power of the models, given the imbalanced distribution of unwanted pregnancy rates in the data. We have proposed a weighted variable to eliminate the effect of under sampling. Among the machine learning approaches tested, Random Forest stands out as the strongest for predicting unwanted pregnancies, delivering 80.35% accuracy and an AUC score of 0.86 on the test data. The model outputs suggest that unmet need for contraception, knowledge of contraception methods, age at first birth, wealth status, total children ever born, knowledge of ovulation cycle, place of residence, woman’s education, woman’s age, and marital duration are the strongest predictors to estimate the unwanted pregnancy. The trained model has also been validated and performed well on recent NFHS-5 data (2019-21). The model can be deployed by policymakers in the sub-regions of India in the future to predict the prevalence of unwanted pregnancy and run a customized campaign to reduce the events of unintended pregnancy through various medical and social interventions.