Naslov (eng)

Thrombophilia Prediction Using Machine Learning Algorithms

Autor

Marovac, Ulfeta
Avdić, Aldina
Đorđević, Nataša
Babić, Goran
Memić, Lejlija
Dolićanin, Zana

Opis (eng)

Abstract: Thrombophilia in pregnancy is the result of a complex interaction of inherited and acquired factors, which increase blood coagulation and consequently placental ischemic conditions. Early identification of risk of developing thrombophilia in pregnancy is crucial for implementing preventive measures and personalized therapy. In this study, we propose a novel approach for prediction of thrombophilia in pregnancy utilizing machine learning (ML) algorithms with a particular focus on neural networks. The research is done using a dataset consisting of demographic, lifestyle, and clinical information from a 35 pregnant woman (22 healthy and 13 with thrombophilia). These features are used to train and evaluate different ML models with neural networks and decision trees. The evaluation of the proposed approach involves cross-validation and performance metrics assessment. The results highlight the effectiveness of decision trees and neural networks in accurately predicting thrombophilia in pregnancy risk.

Jezik

engleski

Datum

2023

Licenca

© All rights reserved

Predmet

Keywords: neural networks, decision trees, machine learning, thrombophilia in pregnancy, prediction

Deo kolekcije (1)

o:28516

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