基于大数据分析的心血管疾病集成预测

D. Krithika, K. Rohini
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摘要

心脏病是导致死亡的主要原因。在我们的身体里,心脏是工作最辛苦的器官。未来患心血管疾病的危险因素是高胆固醇、高血压、糖尿病、家族史强的人。心脏病发作主要是由生活方式引起的。冠心病是冠状动脉,为心脏提供氧气和血液的血管。它几乎是全世界的死因。冠状动脉通常发生在胆固醇积聚在动脉壁形成斑块时。动脉被收紧,血液难以流向心脏。它还会导致菱形的血凝块完全阻塞血液流动,当阻塞发生时,它被称为冠状动脉闭塞。因此,阻塞是心脏病发作的闭塞(心肌梗死)。心肌的收缩突然停止了。血液黏度对维持血管稳态起着重要作用。红细胞压积是血液中堆积的细胞体积。很少有机器学习算法应用于大量数据和准确性检查。所提出的CVD预测模型采用了极端梯度增强、DT、KNN、SVM、Naïve贝叶斯、随机森林、人工神经网络、超参数调谐随机森林算法。
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Ensemble Based Prediction of Cardiovascular Disease Using Bigdata analytics
Heart attack is leading cause of death. In our body heart is hardest working organ. Risk factors for future cardiovascular is people with high cholesterol, high blood pressure, diabetes, strong family history. Heart attack is primarily caused by lifestyle. CAD is coronary arteries blood vessels supply oxygen and blood to the heart.it is almost cause of death worldwide. Coronary arteries normally happen when deposition of cholesterol accumulate artery walls creating plaques. Arteries being tighten and difficult to blood flow to the heart. It also causes blood clots of rhombus totally occludes blood flow, when a blockage occur it is called coronary occlusion. So, blockages are occlusion (myocardial infarction) of heart attack. contraction of heart muscle suddenly stopped. Blood viscosity is important role to maintain vascular homeostasis. Hematocrit is the packed cell volume (pcv) of blood. Few machine learning algorithms applied to large amount of data and accuracy checked. The proposed model is CVD prediction using Extreme Gradient Boost, DT, KNN, SVM, Naïve bayes, Random forest, ANN, Hyper parameter tunned random forest Algorithm.
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