New Rule-Based Model to Predict Maternal Mortality Risk

Authors

Elijah Beauchamp, Fort Hays State UniversityFollow

Classification

Empirical Undergraduate

Abstract

Most maternal deaths occur by preventable means [1]. This could be addressed by finding better ways to track the vitals of mothers and better ways of determining maternal mortality risk. This paper uses an association rule mining (ARM) algorithm called Apriori to generate human readable rules to better classify maternal mortality risk. The Waikato Environment for Knowledge Analysis (WEKA) was used to apply the Apriori algorithm to the data set and python was used to clean the data set. The data set that was used consisted of real maternal health vitals that were then converted into categorical data to apply the Apriori algorithm.

Faculty Advisor

Dr. Anas Hourani

Department/Program

KAMS

Submission Type

in-person poster

DOI

10.58809/VGEG6593

Date

4-17-2023

Rights

Copyright the Author(s)

Comments

For questions contact ScholarsRepository@fhsu.edu

Recommended Citation

Beauchamp, Elijah (2023) "New Rule-Based Model to Predict Maternal Mortality Risk," SACAD: John Heinrichs Scholarly and Creative Activity Days: Vol. 2023, Article 57.
DOI: 10.58809/VGEG6593
Available at: https://scholars.fhsu.edu/sacad/vol2023/iss2023/57