Novel lung cancer detection using ANN classifier in comparison with Decision Tree to measure the Accuracy, Sensitivity, Specificity and Precision

D. Preethi, K. Ganapathy
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引用次数: 1

Abstract

The aim of this work is to predict the performance of the Artificial Neural Network algorithm for novel lung cancer detection. A total of 1339 samples are collected from two lung cancer datasets found in Kaggle. The G power for samples is calculated from clincalc which contains two different groups from which group 1 is taken as ($\mathrm{n}1=670$) and for group 2 ($\mathrm{n}2= 670$), alpha (0.05), power (80%) and enrollment ratio. The collected samples are divided into training dataset $(\mathrm{n}=937 [75\%])$ and test dataset $(\mathrm{n}=402\ [25\%])$. Accuracy, sensitivity, specificity and precision score values are calculated for evaluating the performance of the Artificial Neural Network algorithm. By comparing these two algorithms Artificial Neural Network had given better accuracy, specificity, sensitivity and precision of 97.95%, 96.55%, 98.55% and 98.55% than Decision Tree of 61.22%, 40.90%, 67.10% and 71.68%. By using the SPSS tool, the Significance value is calculated as 0.02. From this proposed work it is observed that the Artificial Neural Network (ANN) had given better accuracy than the Decision Tree algorithm.
用人工神经网络分类器与决策树进行肺癌检测的准确性、敏感性、特异性和精密度的比较
这项工作的目的是预测新型肺癌检测的人工神经网络算法的性能。从Kaggle发现的两个肺癌数据集中共收集了1339个样本。样本的G幂由clincalc计算,clincalc包含两个不同的组,其中第1组取($\mathrm{n}1=670$),第2组取($\mathrm{n}2= 670$) alpha(0.05)、幂(80%)和入组比。将收集到的样本分为训练数据集$(\mathrm{n}=937[75\%])$和测试数据集$(\mathrm{n}=402\[25\%])$。计算了人工神经网络算法的准确度、灵敏度、特异性和精密度评分值,以评价该算法的性能。对比两种算法,人工神经网络的准确率、特异度、灵敏度和精密度分别为97.95%、96.55%、98.55%和98.55%,优于决策树的61.22%、40.90%、67.10%和71.68%。通过SPSS统计工具计算,显著性值为0.02。研究结果表明,人工神经网络(ANN)比决策树算法具有更好的准确率。
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