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首页> 外文期刊>New horizons in translational medicine. >PSO-ANN based diagnostic model for the early detection of dengue disease
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PSO-ANN based diagnostic model for the early detection of dengue disease

机译：基于PSO-Ann基于诊断模型，用于早期检测登革病

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Abstract Large numbers of machine learning approaches have been developed for analysis of medical data in recent years. These approaches have also proved their significance through accurate and earlier diagnosis of diseases. The objective of this work is to develop a diagnostic model for earlier diagnosis of dengue disease. Dengue fever is spread through the bite of the female mosquito (Aedes aegypti). The symptoms of this fever are similar to other fever such as that of Viral influenza, Chikungunya, Zika fever, and so on. However, in this fever, human life can be at risk due to severe depletion of blood platelets. Therefore, early diagnosis of dengue disease can help in protecting human lives by making a preventive move before it turns into an infectious disease. In this work, an effort is made to develop a PSO-ANN based diagnostic model for earlier diagnosis of dengue fever. In the proposed model, PSO technique is applied to optimize the weight and bias parameters of ANN method. Further, PSO optimized ANN approach is used to detect dengue patients. The effectiveness of the proposed model is evaluated based on accuracy, sensitivity, specificity, error rate and AUC parameters. The results of the proposed model have been compared with other existing approaches like ANN, DT, NB, and PSO. It is observed that the proposed diagnostic model is a proficient and powerful model for more accurate and earlier detection of dengue fever. Highlights ? To determine various clinical and non-clinical parameters of dengue fever. ? Real world data is collected for dengue disease from different hospitals of Delhi region. ? To develop a diagnostic model for earlier detection of dengue fever using PSO and ANN techniques. ? Provides more accurate results than other methods.
机译：摘要近年来为医学数据进行了大量的机器学习方法。这些方法还通过准确和早期诊断疾病证明了其重要性。这项工作的目的是开发一种诊断模型，初步诊断登革热病。登革热通过雌性蚊子（AEDES AEGYPTI）叮咬传播。这种发烧的症状类似于其他发烧，如病毒感流感，Chikungunya，Zika发烧等等。然而，在这种发烧中，由于严重的血小板耗尽，人类的生命可能存在风险。因此，登革热病的早期诊断可以通过在进入传染病之前进行预防措施来帮助保护人类生命。在这项工作中，努力开发基于PSO-ANN基于PSO-ANN的诊断模型，以便于登革热的早期诊断。在所提出的模型中，应用PSO技术来优化ANN方法的权重和偏置参数。此外，PSO优化的ANN方法用于检测登革热患者。基于精度，灵敏度，特异性，错误率和AUC参数评估所提出的模型的有效性。将所提出的模型的结果与ANN，DT，NB和PSO等其他现有方法进行比较。据观察，拟议的诊断模型是熟练而强大的模型，以便更准确地检测登革热。强调 ？确定登革热的各种临床和非临床参数。还从德里地区的不同医院收集现实世界数据。还使用PSO和ANN技术开发初期检测登革热的诊断模型。还提供比其他方法更准确的结果。

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