A MODEL FOR PREDICTION OF DRUG RESISTANT TUBERCULOSIS USING DATA MINING TECHNIQUE

A. Halliru, G. Wajiga, Y. M. Malgwi, Abba Hamman Maidabara
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Abstract

The rate of mortality in the recent time because of tuberculosis disease is so alarming. Drug-Resistant Tuberculosis is a communicable disease very dangerous that attack lungs, many victims were not identified due to weak health systems facilities, poor doctor-patient relationship, and inefficient mechanisms for predicting of the disease. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as delay taking during prediction which reduce the treatment cost of a patience. Several data mining technique such as classification, clustering, regression, and association rule were used to enhance the prediction of tuberculosis. In this project I used Naïve Bayes Classifier to design a model for predicting tuberculosis. I considered the following parameters; Gender, Chills, Fever, Night sweat, Fatigue, Cough with Blood, Weight loss, and Loss of Appetite for classification phase 1. While Gender Chest Pain, Sputum, Contact DR, Weight Loss, In-adequate treatment for classification phase 2 as the clinical symptom. The Naïve Bayes Classifier has the advantage of attribute independency, it is easy in construction, can classify categorical data, and can work on high dimensional data effectively. The model designed using Naïve Bayes Classifier is divided o into classification phase 1 and classification phase 2 and implemented using Phython 3.2 Programing Language. The result shows that Naïve Bayes Classfier was suitable in predicting drug resistant tuberculosis with performance accuracy of 82%, 98% and area under curve (AUC) is 88%. Keywords: Model Prediction, Tuberculosis. Drug, Resistant, Data Mining.
基于数据挖掘技术的耐药结核病预测模型
近年来肺结核的死亡率是如此惊人。耐药结核病是一种攻击肺部的非常危险的传染病,由于卫生系统设施薄弱、医患关系差以及疾病预测机制效率低下,许多受害者没有得到确认。数据挖掘可以应用于医疗数据,以预测新的、有用的和潜在的知识,这些知识可以挽救生命,降低治疗成本,提高诊断和预测的准确性,以及在预测期间延迟服用,从而降低患者的治疗成本。采用了分类、聚类、回归、关联规则等数据挖掘技术,提高了结核病的预测能力。在这个项目中,我使用Naïve贝叶斯分类器来设计一个预测结核病的模型。我考虑了以下参数;性别,寒战,发热,盗汗,疲劳,带血咳嗽,体重减轻,食欲不振为第一阶段。而性别胸痛、痰多、接触性DR、体重减轻、治疗不充分为临床症状分类2期。Naïve贝叶斯分类器具有属性独立、构造简单、能对分类数据进行分类、能有效处理高维数据等优点。使用Naïve贝叶斯分类器设计的模型分为分类阶段1和分类阶段2,使用python 3.2编程语言实现。结果表明,Naïve贝叶斯分类器预测耐药结核病的准确率分别为82%、98%,曲线下面积(AUC)为88%。关键词:模型预测;肺结核;药物,抗药性,数据挖掘。
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