Feature Selection Using Automatic Programming Methods in Hypertension Risk Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{YaGmurcu:2024:IDAP,

• author = "Merve YaGmurcu and Sibel Arslan",
• title = "Feature Selection Using Automatic Programming Methods in Hypertension Risk Prediction",
• booktitle = "2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)",
• year = "2024",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Hypertension, Support vector machines, Performance evaluation, Analytical models, Automatic programming, Simulation, Stroke (medical condition), Plasmas, Random forests, Immune system, Artificial Bee Colony Programming, Immune Plasma Programming",
• DOI = "doi:10.1109/IDAP64064.2024.10711046",
• abstract = "Hypertension is a condition where the pressure in the blood vessels is higher than normal. It can lead to serious problems such as heart attack, stroke, heart failure, kidney disease and vision problems. Therefore, early diagnosis and treatment is important to find appropriate treatment strategies for the disease. In this study, automatic programming (AP) methods, were used and compared to analyse the risk of hypertension. These methods are Artificial Bee Colony Programming developed from the behaviour of honeybees, Genetic Programming (GP) inspired by genetic selection and Immune Plasma Programming (IPP) based on immune plasma therapy. According to the performance evaluations obtained from the methods, GP and IPP were the most successful methods with test success rates of 0.91percent and 0.89percent respectively. In future research, Due to the success of the AP methods, we aim to develop different versions for health problems.",
• notes = "Also known as \cite{10711046}",

}

Genetic Programming entries for Merve YaGmurcu Sibel Arslan

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