Accessible Screening for Preeclampsia
SDGs Impacted

TeamArshia Ajmera aajme2@illinois.edu
Keerthana Nallamotu kn19@illinois.edu

Scope/Topic: Health

The Challenge
What is Preeclampsia?
Preeclampsia is a condition associated with continuous high bloodpressure levels that affects women during or after pregnancy. The
condition typically starts around twenty weeks into pregnancy and, if
left undiagnosed, can lead to fatal complications including stroke,
seizures, and organ failure in both the mother and the baby.

Symptoms and Risks- Shortness of breath- Swelling in legs- Persistent nausea- Temporary vision loss
- Severe headaches- Fluid in lungs- Protein in urine- Low platelet count in blood
Many of these symptoms are very common, making preeclampsia hard to identify.

Situational Context
In the United States alone, preeclampsia affects 1 in 25 pregnancies. Nearly 500,000
babies die due to the condition each year. It is also estimated that 10-15% of maternal
deaths are a result of preeclampsia complications.

1 25inpregnancies

500,000
babies die each year

10-15%of maternal deaths

SDG Alignment
Given the high mortality rate associated with preeclampsia, the condition puts the healthand well-being of affected women at significant risk. With a lack of access to technology fortesting for this, there is also a sense of gender inequality as women aren’t able to get
testing for certain health problems. There is also a disparity between countries that do anddon’t have access to reproductive health testing at an affordable cost.
Accessible Screening for Preeclampsia

1Our Solution
Proposed Action
In order to reduce the number of fatalities caused by preeclampsia during pregnancy, weaim to build a more accurate diagnostic system that only requires data from a basic
medical examination.

Current Efforts
Although there have been attempts in the past to use various machine learning models forpreeclampsia identification, underlying issues in the choice of models and features have
limited their generalizability on real-world data. To avoid such issues, we use a random
forest (RF) model to determine the likelihood of preeclampsia symptoms from features
such as maternal demographics and characteristics, maternal medical history, present
medical condition and complications, laboratory data, and nutrition.
Relying on an RF-based algorithm will also allow us to present clinicians with an analysisof explainability metrics that clearly outline which feature thresholds most impacted thefinal decision of the algorithm.

Benefits
We envision this tool augmenting the observations of clinicians by providing an objective
analysis of the patient’s medical data. Since the features we are considering are noninvasive and commonly collected during basic medical check-ups for pregnancy, it will also
assist in identifying signs of preeclampsia in regions where access to more extensive
screenings is limited.

SDG Advancements
Ensure healthy lives and promote well-being for all at all ages
Indicator: Reduce global maternal mortality ratio to less than 70 per 100,000 live births
By implementing a way to test for preeclampsia, we can help reduce the maternalmortality rate in many different countries that struggle with it.

Achieve gender equality and empower all women and girls
Indicator: Ensure universal access to sexual/reproductive health and reproductive rights
By making the testing with data from medical exams, it help ensure that the tests canbe provided universally.

Reduce inequality within and among countries
Indicator: Implement special and differential treatment for developing countries
With basic testing, it helps lower the costs of the examination, therefore allowing it to beaccessible to those in developing countries to be able to afford.
Accessible Screening for Preeclampsia

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Implementation
Feasibility
We plan to first roll out our product to hospitals in the US and then associate with medical volunteer
organizations that can bring it to medical centers in developing nations where access to moresophisticated pregnancy screenings is limited. We will use the number of preeclampsia cases correctly
diagnosed by the tool as our metric of impact.

Timeline
It will take approximately five months to complete the development phase of the solution, which would
include building both the underlying machine learning diagnostic model and the user-facing web
application. Another three months would be spent reaching out to hospitals and validating model results.
After this stage, we will continue working on increasing the accuracy of our algorithm while reaching out toorganizations that would be able to expand the reach of the tool to other regions of the world.

Resources
Computational power is one resource would support the tool's performance. Given the large number offeatures our model is considering, a more powerful CPU would be required to ensure that predictions canbe made within a reasonable amount of time. Machine learning engineers and web developers areanother key resource that would accelerate the development timeline for the tool.

Partnerships
We plan to partner with hospitals and organizations such as the Preeclampsia Foundation that arededicated to fighting the condition in order to test our solution in a real-world setting. Based on thesevalidation experiments, we will be able to make improvements to the core model that would increase its
overall precision and efficiency.

Obstacles
The lack of publicly available data is one obstacle that may initially limit the accuracy of the diagnostic
tool. However, by establishing hospital partnerships, we can collect more extensive and updated data toincrease prediction reliability.

Conclusion
Preeclampsia is a deadly condition that affects a significant number of pregnant
women around the world each year. However, with the help of our diagnostic
tool, doctors will be able to identify signs of the disease from an early stage andtreat its symptoms before they become life-threatening.
Acknowledgement: Puja Sanghvi Gupta, MD | Director of Rehabilitation @ Recovre | pujajs@gmail.com
References:“Home - Preeclampsia Foundation.” Preeclampsia Foundation - Saving Mothers and Babies from Preeclampsia,
www.preeclampsia.org/.
“Preeclampsia, Genomics and Public Health.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention,
blogs.cdc.gov/genomics/2022/10/25/preeclampsia/#:~:text=Preeclampsia%20is%20estimated%20to%20occur,and%20500%2C000%20
fetal%20deaths%20worldwide.
“Preeclampsia.” Mayo Clinic, Mayo Foundation for Medical Education and Research, www.mayoclinic.org/diseasesconditions/preeclampsia/symptoms-causes/syc-20355745.
Pooja Toshniwal Paharia, Reviewed by Lily Ramsey. “Machine Learning-Based Preeclampsia Risk Prediction.” News, 21 Aug. 2023,
www.news-medical.net/news/20230821/Machine-learning-based-preeclampsia-riskprediction.aspx#:~:text=In%20total%2C%2013%20studies%20were,serum%20protein%20A%3B%20two%20studies.
“Preeclampsia Datasets.” BioGPS, biogps.org/dataset/tag/preeclampsia/.

Accessible Screening for Preeclampsia

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