EMLPGENE: Enhanced MLP Gene Based Multi Disease Detection System Using Heterogeneous Data

M. Venugopal, V. K. Sharma, Kalpana Sharma
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Abstract

The advancement of intelligent learning algorithms made the researchers to develop the generalized models that can handle heterogeneous data. With the post covid, different people are suffering from different type of diseases. Multi disease detection model is needed to prevent or to diagnosis various disease rather using different single detection platforms. In order to develop multi disease platform, the basic analysis lies in the gene structure of the human. All the existing detection systems find the disease based on either general characteristics or symptoms associated with the diseases. Symptoms based model may sometimes fail because of the thin difference between various diseases like continuous cough in case of covid as well as pneumonia or TB. So the proposed model collects the heterogeneous data associated with gene and predicts 8 multiple diseases using the enhanced MLP. Neural networks can handle heterogeneous data with less resources. When compared to the existing machine learning approaches, this model has achieved $+6.4\%$ improvements in terms of accuracy.
EMLPGENE:利用异构数据增强的基于MLP基因的多种疾病检测系统
智能学习算法的进步使得研究人员开发了能够处理异构数据的广义模型。随着新冠疫情的到来,不同的人正在遭受不同类型的疾病。为了预防或诊断多种疾病,需要建立多种疾病检测模型,而不是使用不同的单一检测平台。为了开发多疾病平台,基本的分析在于人类的基因结构。现有的所有检测系统都是根据一般特征或与疾病相关的症状来发现疾病的。基于症状的模型有时可能会失败,因为各种疾病之间的差异很小,例如covid的持续咳嗽以及肺炎或结核病。因此,该模型收集了与基因相关的异质性数据,并利用增强的MLP预测了8种多种疾病。神经网络可以用较少的资源处理异构数据。与现有的机器学习方法相比,该模型在准确性方面取得了+ 6.4%的提高。
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