Optimizing Diabetes Predictive Modeling with Automated Decision Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Albalushi:2023:SWC,

• author = "Muna Albalushi and Rasha {Al Jassim} and Karan Jetly and Raya {Al Khayari} and Hilal {Al Maqbali}",
• booktitle = "2023 IEEE Smart World Congress (SWC)",
• title = "Optimizing Diabetes Predictive Modeling with Automated Decision Trees",
• year = "2023",
• abstract = "This paper introduces Linear Genetic Programming for Optimising Decision Tree (LGP-OptTree), a novel form of Genetic Programming (GP) aimed at enhancing diabetes detection. LGP-OptTree is designed to optimise the attributes and hyperparameters of decision trees by using a unique genotype and phenotype structure. The proposed method is evaluated on the Pima dataset and compared with other techniques. By fine-tuning the attributes and hyperparameters of decision trees using LGP-OptTree, this study aims to improve the accuracy and efficacy of diabetes detection. A performance metric is used to determine the effectiveness of the proposed method with respect to other approaches. The contribution of this research lies in providing general healthcare professionals with a new approach for enhancing diabetes detection accuracy through decision trees.",
• keywords = "genetic algorithms, genetic programming, Measurement, Medical services, Predictive models, Prediction algorithms, Diabetes, Decision trees, Evolutionary Algorithm",
• DOI = "doi:10.1109/SWC57546.2023.10449077",
• month = aug,
• notes = "Also known as \cite{10449077}",

}

Genetic Programming entries for Muna Albalushi Rasha Al Jassim Karan Jetly Raya Al Khayari Hilal Al Maqbali

Citations