决策树与集成模型在慢性肾脏疾病分类中的比较分析

Olawumi Olasunkanmi, O. D. Olanloye, Abdulquadri Adegbiji
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引用次数: 1

摘要

当今世界已进入大数据和大数据处理时代,数据挖掘已成为重大挑战之一。因此,研究人员做了很多工作来分析卫生部门的这些数据,以利用人工智能和机器学习原理加强疾病检测和分类。肾脏疾病是一种可怕的疾病,它的晚发现使许多人过早地进入坟墓。ML分类器已经在许多维度上被用于对心脏病进行分类,但是,现有的工作并没有探索每种方法选择最佳模型参数的变体。本研究试图研究来自两种基于树的肾脏疾病分类模型的三种变体的行为。其中三种变体是决策树分类器的复杂、中等和简单模型,另一种变体是集成分类器的boosting、Bagged和rusboosting。利用MATLAB实现,模型性能证明了集成分类器(Bagged树模型)的准确率最好,在速度方面,决策树(复杂树模型和简单树模型具有相同的最高值)。因此,这两个是最好的。在训练时间方面,决策树(简单树)的训练时间最少,因此是最好的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases
The world is now in the era of big data and processing, and exploring the data has become one of the significant challenges. Hence, researchers have done a lot to analyse these data in the health sector to enhance disease detection and classification using artificial intelligence and ML principles. Kidney disease is one of the terrible conditions in which its late detection has sent many people to untimely graves. ML classifiers have been employed in many dimensions to classify heart disease, but, existing works have not explored the variants of each method for selection of best model parameters. An attempt is being made in this research to study the behaviour of three (3) variants each from two(2) tree-based models in the classification of Kidney Disease. Three of the variants are Complex, Medium and Simple models of Decision tree classifier and the other one are Boosted, Bagged and RUSBoosted of Ensemble Classifiers. Using MATLAB for implementation, the model performance established that the accuracy of Ensemble Classifier (Bagged tree model) is the best, concerning the speed, Decision tree (Complex and Simple tree models have the same and highest value). Hence, the two are the best. In terms of training time, Decision tree(Simple tree) has the least time and therefore the best.
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