使用Logistic回归对肝脏患者进行分类

Syed Hasan Adil, Mansoor Ebrahim, Kamran Raza, Syed Saad Azhar Ali, Manzoor Ahmed Hashmani
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引用次数: 10

摘要

在这篇研究论文中,我们应用机器学习方法根据患者性别和实验室医学检测数据对肝脏患者(即肝脏患者或非肝脏患者)进行分类。标记的数据集在UCI机器学习存储库上发布为“印度肝脏患者记录”。这项工作背后的动机是应用简单且计算量较少的分类技术,如逻辑回归,并将其结果与其他研究人员在相同数据集上获得的早期结果进行比较。逻辑回归的分类结果证明了其在该数据集上的重要性,其分类精度优于Ramana等研究论文中提出的NBC (Naïve Bayes Classifier)、C4.5 (Decision Tree)、SVM (Support Vector Machine)、ANN (Artificial Neural Network)和KNN (K Nearest Neighbors)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Liver Patient Classification using Logistic Regression
In this research paper, we have applied machine learning approach to classify liver patient (i.e., Liver Patient or Not Liver Patient) using patient gender and laboratory medical test data. The labelled dataset was published on UCI machine learning repository as "Indian Liver Patient Records". The motivation behind this work is to apply simple and less computational classification technique like Logistic Regression and compare its results with earlier results obtained on the same dataset by other researchers. The classification results of Logistic regression have proved its significance on this dataset by achieving better classification accuracy than NBC (Naïve Bayes Classifier), C4.5 (Decision Tree), SVM (Support Vector Machine), ANN (Artificial Neural Network), and KNN (K Nearest Neighbors) as presented in Ramana et al., research paper.
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