Forecasting Maternal Women's Health Risks using Random Forest Classifier-Reference-Cited by-同舟云学术

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Forecasting Maternal Women's Health Risks using Random Forest Classifier

Published:2024-04-24 Issue: Volume:9 Page:961-965
ISSN:
Container-title:2024 International Conference on Inventive Computation Technologies (ICICT)
language:
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Author:

Thakkar Dhruvi¹,Gandhi Vaibhav C.²,Trivedi Dhriti³

Affiliation:

1. Drs Kiran & Pallavi Patel Global University (KPGU University),Faculty of Engineering & Technology,Computer Science & Engineering Department,Vadodara,India

2. Charutar Vidya Mandal University (CVM University),Madhuben & Bhanubhai Patel Institute of Technology (MBIT),Computer Engineering Department,Anand,India

3. Gujarat Technological University (GTU University),Sardar Vallabhbhai Patel Institute of Technology (SVIT),Computer Engineering Department,Vadodara,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10543255/10544376/10544471.pdf?arnumber=10544471

Reference26 articles.

1. A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

2. Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data

3. Factors influencing the quality of life of pregnant women: a systematic review

4. Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy

5. Prediction of Maternal Hemorrhage Using Machine Learning: Retrospective Cohort Study

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