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首页> 外文期刊>International journal of engineering research and industrial applications >UTILIZATION OF DATA MINING TECHNIQUES FOR PREDICTION AND DIAGNOSIS OF AIDS/HIV DISEASE SURVIVABILITY
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UTILIZATION OF DATA MINING TECHNIQUES FOR PREDICTION AND DIAGNOSIS OF AIDS/HIV DISEASE SURVIVABILITY

机译：利用数据挖掘技术预测和诊断AIDS / HIV疾病的生存能力

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摘要

Human immunodeficiency virus (HIV) is a lent virus that causes acquired immunodeficiency syndrome (AIDS). The main drawback in HIV treatment process is its sub type prediction. The sub type and group classification of HIV is based on its genetic variability and location. HIV can be divided into two major types, HIV type 1 (HIV-1) and HIV type 2 (HIV-2). Many classifier approaches have been used to classify HIV subtypes based on their group, but some of cases are having two groups in one; in such cases the classification becomes more complex. The methodology used is this paper based on the HIV sequences. For this work several classifier approaches are used to classify the HIV1 and HIV2. For implementation of the work a real time patient database is taken and the patient records are experimented and the final best classifier is identified with quick response time and least error rate. A typical confusion matrix is furthermore displayed for quick check. The study describes algorithmic discussion of the HIV dataset from AVERT's Community database, on line repository of large datasets. The Best results are achieved by using Tanagra tool. Tanagra is data mining matching set. The accuracy is calculate based on addition of true positive and true negative followed by the division of all possibilities.
机译：人类免疫缺陷病毒（HIV）是一种导致获得性免疫缺陷综合症（AIDS）的慢病毒。 HIV治疗过程的主要缺点是其亚型预测。 HIV的亚型和组分类基于其遗传变异性和位置。 HIV可以分为两种主要类型，HIV 1型（HIV-1）和HIV 2型（HIV-2）。已经使用了许多分类器方法来根据其亚型对HIV亚型进行分类，但是有些情况是将两个亚类合为一组。在这种情况下，分类变得更加复杂。本文使用的方法是基于HIV序列的本文。对于这项工作，使用了几种分类器方法对HIV1和HIV2进行分类。为了进行这项工作，需要建立一个实时的患者数据库，并对患者记录进行实验，并以快速的响应时间和最小的错误率来确定最终的最佳分类器。此外，还会显示一个典型的混淆矩阵，以便快速检查。该研究描述了AVERT社区数据库中大型数据集在线存储库中HIV数据集的算法讨论。使用Tanagra工具可获得最佳结果。 Tanagra是数据挖掘匹配集。准确度的计算是基于真实正值和真实负值相加，然后除以所有可能性。

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