使用定制的神经握手识别系统

L. Putra, Eka Kusumawardhani, Putranty Widha Nugraheni, Lalak Tarbiyatun Nasyin Maleiva, Vincentius Abdi Gunawan
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引用次数: 0

摘要

心脏病是世界上死亡率第二高的疾病。这是人类不健康的生活方式造成的。这种不健康的生活方式会影响身体器官的功能。中风可以通过经常锻炼、吃有营养的食物、不喝酒和不吸烟来预防。一种发现某人是否没有中风的方法是通过医学检查。然而,这种方法非常昂贵。鉴于这些问题,本研究旨在设计一个早期识别系统来检测早期中风。系统的设计是利用主体的状况和历史进行身份识别。本研究使用反向传播神经网络进行分类过程。为了在训练过程中获得最高的准确率,我们利用了每个实验中隐藏层使用的变化。从研究结果来看,所设计的系统可以检测出早期脑卒中,准确率为97.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SISTEM IDENTIFIKASI DINI PENYAKIT STROKE DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN PERAMBATAN BALIK
Heart disease is a disease with the second-highest mortality rate in the world. This happens because of an unhealthy human lifestyle. This unhealthy lifestyle affects the performance of the body's organs in carrying out their functions. Stroke can be prevented by exercising regularly, eating nutritious foods, not consuming alcohol, and not consuming tobacco. One way to find out if someone is free from stroke or not can be done by medical check-ups. However, this method is quite expensive. Given these problems, this study aims to design an early identification system for detecting early-stage stroke. The system is designed by utilizing the condition and history of the subject for identification. This study uses a back propagation neural network for the classification process. Variations in the use of hidden layers in each experiment were used to obtain the highest accuracy in the training process. From the results of the study, it was found that the system designed can detect early stroke with an accuracy rate of 97.8%.
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