Department of Computer Science and Engineering, National Institute of Technology Goa, India.
Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc. Roseville, CA, USA,
Front Biosci (Landmark Ed). 2020 Mar 1;25(7):1202-1229. doi: 10.2741/4853.
This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers including Quadratic Integrate and Fire neuron (QIFN) model as a benchmark. As per research studies, parasite transmission is highly dependent on the (i) stagnant water, (ii) population of area and the (iii) greenery of the locality. Considering these factors, three more attributes were added to the existing novel dataset and comparison on the results is presented. For four feature dataset, QIFN exhibited an accuracy of 97.08% in K10 protocol, and with extended dataset; QIFN yields an accuracy of 99.58% in K10 protocol. The benchmarking results showed reliability and stability. There is 12.47% improvement against multilayer perceptron (MLP) and 5.39% against integrate-and-fire neuron (IFN) model. The QIFN model performed the best over the conventional classifiers for deciphering the risk of acquiring malaria in different geographical regions worldwide.
（a）气象因素；（b）人口统计学；（c）患者信息。研究在扩展数据集上进行，对包括二次积分和放电神经元（QIFN）模型在内的扩展特征和非扩展特征进行了 Spike 和非 Spike 分类器的观察。根据研究，寄生虫传播高度依赖于（i）死水、（ii）地区人口和（iii）当地的绿化情况。考虑到这些因素，我们在现有的新型数据集上增加了另外三个属性，并对结果进行了比较。对于四个特征数据集，QIFN 在 K10 协议中的准确率为 97.08%，而在扩展数据集上，QIFN 的准确率为 99.58%。基准测试结果表明了其可靠性和稳定性。与多层感知机（MLP）相比，QIFN 提高了 12.47%，与积分和放电神经元（IFN）模型相比，提高了 5.39%。在不同地理区域识别疟疾感染风险方面，QIFN 模型比传统分类器表现更为出色。
