Department of Computer Science and Engineering, National Institute of Technology Goa, India.
Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India.
Front Biosci (Landmark Ed). 2020 Jan 1;25(2):299-334. doi: 10.2741/4808.
Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classified as low and high risk regions using a backpropagation neural network (BPNN). However, this approach yielded low performance and stability necessitating development of a more robust model. We hypothesized that by spiking neuron models in simulating the characteristics of a neuron, which when embedded with a BPNN, could improve the performance for the assessment of malaria prone regions. To this end, we created an inter-spike interval (ISI)-based BPNN (ISI-BPNN) architecture that uses a single-pass spiking learning strategy and has a parallel structure that is useful for non-linear regression tasks. Existing malaria dataset comprised of 1296 records, that met these attributes, were used. ISI-BPNN showed superior performance, and a high accuracy. The benchmarking results showed reliability and stability and an improvement of 11.9% against a multilayer perceptron and 9.19% against integrate-and-fire neuron models. The ISI-BPNN model is well suited for deciphering the risk of acquiring malaria as well as other diseases in prone regions of the world.
疟疾是一种由疟原虫属的寄生虫引起的传染病。这些寄生虫通过蚊子传播，蚊子在世界某些地区很常见。根据它们特定的气候，这些地区使用反向传播神经网络（BPNN）被分类为低风险和高风险地区。然而，这种方法的性能和稳定性较低，因此需要开发更稳健的模型。我们假设，通过在模拟神经元特征的尖峰神经元模型中嵌入 BPNN，可以提高评估疟疾流行地区的性能。为此，我们创建了一种基于尖峰间隔（ISI）的 BPNN（ISI-BPNN）架构，该架构使用单步尖峰学习策略和并行结构，非常适合非线性回归任务。使用了符合这些属性的现有的疟疾数据集，共 1296 条记录。ISI-BPNN 表现出优越的性能和高精度。基准测试结果显示了可靠性和稳定性，并与多层感知器相比提高了 11.9%，与积分和点火神经元模型相比提高了 9.19%。ISI-BPNN 模型非常适合破译在世界疟疾流行地区感染疟疾和其他疾病的风险。
