Applied Statistics and Data Science Seminar on Thursday 05 March 2026

Title: FertiMeter: A Data-Driven Innovation to Address the Reproductive Health Crisis of Polycystic
Ovary Syndrome

Venue, date and time: ISRT, 5 March 2026, 12:15 pm

Speaker: K. M. Tanvir, Lecturer, ISRT, University of Dhaka

Abstract:

Background:
Polycystic ovary syndrome (PCOS) affects around 12.5% of women in Bangladesh and is a major cause of infertility and pregnancy complications. Although early detection can help manage symptoms and reduce risks, nearly 70% of women remain undiagnosed due to limited awareness and inadequate access
to medical care.
Objectives:
This study aims to develop a data-driven machine learning model that predicts the likelihood of PCOS using non-clinical features and to integrate it into a mobile application, FertiMeter.
Methods:
A total of 546 participants, including 273 women diagnosed with PCOS and 273 without PCOS, were enrolled in the study. The CatBoost machine learning algorithm was applied to develop a predictive model for PCOS status and the model was incorporated into the FertiMeter mobile application.
Key Findings:
Using eight SHAP-selected non-clinical features, the CatBoost model achieved an average cross-validated accuracy of 86%.
Conclusions:
Approximately 6.7 million women in Bangladesh who remain undiagnosed with PCOS can use FertiMeter mobile application to assess their likelihood of having the condition free of cost.