基于混合人工智能的心血管疾病检测算法设计

Buse Nur Karaman, Zeynep Bağdatli, Nilay Nisa Taçyildiz, Sude Çi̇ğni̇taş, Derya Kandaz, M. K. Ucar
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摘要

目的:心血管疾病(CVD)是一种由于血管内壁积聚形成斑块而对血管系统产生负面影响的疾病。在诊断阶段,血管造影结果由医生评估。由于CVD诊断方法耗时且成本高,因此需要基于人工智能的新诊断算法,包括新技术。材料和方法:研究中使用了开源共享网站Kaggle上的心脏病数据集。该数据集包括14项临床发现。在研究中,在使用Fischer特征选择算法选择特征后,使用集成决策树(EDT)、k近邻算法(kNN)和神经网络(NN)对特征进行分类。利用这三种方法建立了一种混合人工智能算法。结果:根据分类结果,EDT %96.19, kNN %100, NN %86.17,混合人工智能诊断CVD的成功率为%99.3。结论:根据所得结果,评价所提出的CVD诊断混合人工智能算法可用于实际
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HYBRID ARTIFICIAL INTELLIGENCE-BASED ALGORITHM DESIGN FOR CARDIOVASCULAR DISEASE DETECTION
Objective: Cardiovascular Disease (CVD) is a disease that negatively affects the blood vessel system due to plaque formation as a result of accumulation on the inner wall of the vessels. In the diagnostic phase, angiography results are evaluated by physicians. New diagnostic algorithms based on artificial intelligence, including new technologies, are needed for diagnosing CVD due to the time-consuming and high cost of diagnostic methods. Materials and Methods: The heart disease dataset available on the open-source sharing site Kaggle was used in the study. The dataset includes 14 clinical findings. In the study, after the features were selected with the Fischer feature selection algorithm, they were classified with Ensemble Decision Trees (EDT), k-Nearest Neighborhood Algorithm (kNN), and Neural Networks (NN). A hybrid artificial intelligence algorithm was also created using the three methods. Results: According to the classification results, EDT %96.19, kNN %100, NN %86.17, and hybrid artificial intelligence determined CVD with a %99.3 success rate. Conclusion: According to the obtained results, it is evaluated that the proposed CVD diagnosis hybrid artificial intelligence algorithms can be used in practice
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