基于分布式高性能轻型GBM的心脏病预测模型

B. Chowdary, Jaina Kedarnath, R. Vyshnavi, Valluri Lavakush, Chavula Shashidhar
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引用次数: 1

摘要

医疗保健行业获取了大量包含一些隐藏信息的事实;它实际上对做出强大的选择很有用。为了给出恰当的结果,并在记录上做出可靠的选择,使用了一些现代的增强事实的策略。在本研究中,使用分布式高性能轻度GBM预测冠心病的风险程度优于其他可靠的心脏病预测版本。该设备利用年龄、性别、血压过高、低密度脂蛋白胆固醇、体重问题等14项临床指标进行预测。该模型预测了患者患冠心病的可能性。它允许大的记录。与冠心病相关的临床因素之间的关系,类似于风格,有待安装。由于训练装置的存在,采用了适度的边坡改良技术。实验结果表明,所设计的诊断装置能够有效地预测心血管疾病的威胁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Effective and Efficient Heart Disease Prediction Model Using Distributed High Performance Light GBM
The healthcare industries acquire massive portions of facts which encompass some hidden information; it virtually is useful for making powerful options. For giving appropriate results as nicely as making dependable options on records, some modern-day-day facts enhancing strategies are used. In this check, A Reliable and additionally Reliable Heart Disease Prediction Version the usage of Distributed High Performance mild GBM is superior for predicting the risk diploma of coronary heart problem. The device makes use of 14 clinical specs collectively with age, sex, immoderate blood strain, ldl cholesterol, weight troubles and so on for prediction. The model anticipates the possibility of sufferers obtaining coronary heart trouble. It allows big records. E.G. Relationships in amongst clinical elements associated with coronary heart sickness similarly to styles, to be installation. The moderate slope improving technique has been used due to the training device. The received consequences have shown that the designed diagnostic device can efficaciously expect the threat of cardiovascular illness.
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