Enhancing Fetal Health Monitoring through TPOT and Optuna in Machine Learning-Driven Prenatal Care

Created by W.Langdon from gp-bibliography.bib Revision:1.8987

@InProceedings{Akilandeswari:2024:APCI,

• author = "A. Akilandeswari and Arasuraja G and Nagendar Yamsani and S. Radhika and N. Legapriyadharshini and S. Padmakala",
• title = "Enhancing Fetal Health Monitoring through {TPOT} and {Optuna} in Machine Learning-Driven Prenatal Care",
• booktitle = "2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI)",
• year = "2024",
• address = "Kannur, India",
• month = "21-22 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Analytical models, Accuracy, Refining, Machine learning, Predictive models, Boosting, Medical diagnosis, Fetal Health Monitoring, Machine Learning in Prenatal Care, Cardiotocogram Analysis, Hyper parameter Optimisation, Automated Machine Learning (AutoML), Predictive Modelling in Healthcare",
• isbn13 = "979-8-3503-6329-6",
• DOI = "10.1109/APCI61480.2024.10617339",
• abstract = "We delve into the application of advanced machine learning techniques for the classification of fetal health, a critical domain in prenatal care. Using a dataset based on cardiotocograms (CTGs), which record key fetal indicators like heart rate and uterine contractions, we compare two distinct machine learning approaches: a Random Forest Classifier optimised with the hyper parameter tuning tool Optuna, and a genetic programming-based model developed using TPOT (Tree-based Pipeline Optimisation Tool). The Random Forest Classifier, configured with specific hyper parameters, delivered an accuracy of 94.13 percent and an impressive AUC of 0.9826. In contrast, the TPOT-optimised model, a Gradient Boosting Classifier with finely tuned parameters, achieved a higher accuracy of 96.01 percent and an internal CV score of approximately 95.24 percent. This comparison underscores the strengths and potential applications of these advanced methodologies in predicting and ensuring fetal health.",
• notes = "Also known as \cite{10617339}",

}

Genetic Programming entries for A Akilandeswari Arasuraja G Nagendar Yamsani S Radhika N Legapriyadharshini S Padmakala

Citations