Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network
Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network
复制标题
DOI：
10.1007/s11517-010-0669-z
复制
发表时间：
2010-11-01
影响因子：
3.2
通讯作者：
Taib, M. N.
中科院分区：
文献类型：
--
作者：
Ibrahim, F.;Faisal, T.;Taib, M. N.
本文提出了一种使用生物电阻抗分析（BIA）和人工神经网络（ANN）对登革热患者早期风险进行诊断和分类的新方法。共有 223 名健康受试者和 207 名住院登革热患者进行了前瞻性研究。登革热风险严重程度标准是根据三项血液检查确定和分组的，即血小板（PLT）计数（小于或等于30,000个细胞/mm（3））、血细胞比容（HCT）（增加超过或等于20％）以及天冬氨酸转氨酶（AST）水平（提高到正常上限的五倍）或丙氨酸转氨酶（ALT）水平（提高到正常上限的五倍）。根据风险组对登革热患者进行分类，随后获得并量化相应的 BIA 参数。使用四个参数来训练和测试 ANN，即发烧日、电抗、性别和风险组的量化。发烧日定义为发烧消退日，即体温降至37.5A摄氏度以下时。血液检查和BIA数据采集5天。使用对数 sigmoid 传递函数通过最速下降反向传播和动量算法对 ANN 进行训练，同时将平方和误差用作网络的性能指标。使用权重消除方法对 3-6-1（3 个输入、6 个隐藏层神经元和 1 个输出）、学习率为 0.1、动量常数为 0.2、迭代率为 20,000 次的最佳 ANN 架构进行剪枝。消除 0.05 的权重可将登革热的预测风险分类准确率提高到高风险组的 95.88% 和低风险组的 96.83%。结果，该系统能够对登革热患者的风险进行分类和诊断，总体预测准确率达到96.27%。
This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5A degrees C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.