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Anya Deshpande: Unveiling Fertility Trends Through AI Insights
Anya Deshpande/LinkedIn
Nov 23, 2025, 03:04

Anya Deshpande: Unveiling Fertility Trends Through AI Insights

Anya Deshpande, President of Monta Vista DesignIt Club, shared a post on LinkedIn:

“Exciting news! I’m thrilled to share that my research paper titled ‘Data-Driven Insights into Fertility Trends: An Explainable AI Approach to Forecasting and Policy Implications’ has been published in the National High School Journal of Science.

What I did:

  • Leveraged a combination of the time-series forecasting model Prophet and the machine-learning model XGBoost, in tandem with interpretability techniques using SHAP (SHapley Additive explanations), to study fertility patterns in two U.S. states (California & Texas) from 1973 to 2020.
  • found that Prophet outperformed a linear regression baseline, achieving very low error rates (e.g., MAPE < 1% for California) in forecasting annual births.
  • Through SHAP-based interpretability, I showed that variables such as miscarriage totals, abortion access, and state-level policy differences emerged as the most influential predictors of fertility outcomes.
  • forecasts for both states project long-term declines in births, pregnancies, abortions and miscarriages through 2030 — with California showing smoother decline and Texas displaying sharper fluctuations, suggesting that state policy and healthcare access matter deeply.

Why this matters

  1. Bridging accuracy + transparency. It’s not enough to forecast; decision-makers need to know why the forecasts behave as they do. Explainable AI (XAI) helps open the “black box”.
  2. Informing reproductive health policy. The findings highlight how access to reproductive care, miscarriage trends, and state policy contexts are tightly connected to fertility outcomes. This offers actionable insights for health planners and policymakers.
  3. Intersection of tech + health. This project is a direct extension of my passion for using computational biology, data science and UX/human-computer interaction tools to improve women’s health — combining technical rigor with real-world impact.

A big thank you to:

  • The open-data community (especially the Open Science Framework dataset) for enabling this work.
  • My peers, advisors, and the editorial team at NHSJS for supporting the publication process

Thank you for reading, and I’d love to connect with anyone interested in fertility research, AI for health, explainable machine learning, or interdisciplinary tech-for-good projects.”

Stay updated on all scientific advances in the field of fertility with Fertility News.

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