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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries
Expert Systems ( IF 3.0 ) Pub Date : 2020-06-14 , DOI: 10.1111/exsy.12573
Roohallah Alizadehsani 1 , Mohamad Roshanzamir 2 , Moloud Abdar 1 , Adham Beykikhoshk 3 , Abbas Khosravi 1 , Saeid Nahavandi 1 , Pawel Plawiak 4 , Ru San Tan 5 , U Rajendra Acharya 6
Affiliation  

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries.

中文翻译:

混合遗传离散算法处理冠状动脉狭窄诊断中的数据不确定性

冠状动脉疾病 (CAD) 是全球发病率和死亡的主要原因。侵入性冠状动脉造影是诊断 CAD 最准确的技术,但具有侵入性且成本高。因此,机器学习和数据挖掘技术等分析方法正变得越来越流行。虽然医生需要知道哪些动脉狭窄,但大多数研究人员只关注 CAD 检测,很少有研究分别研究右冠状动脉 (RCA)、左回旋支 (LCX) 和左前降支 (LAD) 动脉的狭窄。同时,该领域的大多数数据集都是嘈杂的(数据不确定性)。然而,据我们所知,没有针对这一重要问题进行的研究。本研究使用 Z-Alizadeh Sani 数据集的扩展,包含 54 个特征的 303 条记录。在这项工作中提出了一种新的特征选择算法。同时,通过数据的离散化,我们还处理了CAD预测中的不确定性。据我们所知,这是第一项尝试处理 CAD 预测不确定性的研究。最后,遗传算法 (GA) 用于确定支持向量机 (SVM) 内核的超参数。我们对每个主要冠状动脉的狭窄诊断都取得了很高的准确性。这项研究的结果可以帮助临床医生验证他们对 RCA、LCX 和 LAD 冠状动脉的手动狭窄诊断。这是第一项尝试处理 CAD 预测不确定性的研究。最后,遗传算法 (GA) 用于确定支持向量机 (SVM) 内核的超参数。我们对每个主要冠状动脉的狭窄诊断都取得了很高的准确性。这项研究的结果可以帮助临床医生验证他们对 RCA、LCX 和 LAD 冠状动脉的手动狭窄诊断。这是第一项尝试处理 CAD 预测不确定性的研究。最后,遗传算法 (GA) 用于确定支持向量机 (SVM) 内核的超参数。我们对每个主要冠状动脉的狭窄诊断都取得了很高的准确性。这项研究的结果可以帮助临床医生验证他们对 RCA、LCX 和 LAD 冠状动脉的手动狭窄诊断。
更新日期:2020-06-14
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