Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification

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@Article{GHANE:2022:bbe,

• author = "Mostafa Ghane and Mei Choo Ang and Mehrbakhsh Nilashi and Shahryar Sorooshian",
• title = "Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification",
• journal = "Biocybernetics and Biomedical Engineering",
• volume = "42",
• number = "3",
• pages = "902--920",
• year = "2022",
• ISSN = "0208-5216",
• DOI = "doi:10.1016/j.bbe.2022.07.002",
• URL = "https://www.sciencedirect.com/science/article/pii/S0208521622000663",
• keywords = "genetic algorithms, genetic programming, Decision tree induction, Decision tree algorithm (J48), Parkinson's disease",
• abstract = "The diagnosis of Parkinson's disease (PD) is important in neurological pathology for appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been widely used for diagnosing PD through biomedical voice disorders. However, DTI for PD diagnosis is based on a greedy search algorithm which causes overfitting and inferior solutions. This paper improved the performance of DTI using evolutionary-based genetic algorithms. The goal was to combine evolutionary techniques, namely, a genetic algorithm (GA) and genetic programming (GP), with a decision tree algorithm (J48) to improve the classification performance. The developed model was applied to a real biomedical dataset for the diagnosis of PD. The results showed that the accuracy of the J48, was improved from 80.51percent to 89.23percent and to 90.76percent using the GA and GP, respectively",

}

Genetic Programming entries for Mostafa Ghane Mei Choo Ang Mehrbakhsh Nilashi Shahryar Sorooshian

Citations