Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE).

译文

:早期诊断乳腺癌对于挽救患者生命至关重要。通常,医学数据集包括各种各样
可能导致诊断过程中混乱的数据。数据库知识发现(KDD)流程有助于
提高效率。在最终诊断之前,需要从数据集中消除不适当和重复的数据。
这可以使用数据挖掘中可用的任何特征选择算法来完成。考虑功能选择
作为提高分类准确性的重要步骤。本文针对特征提出了一种改进的Bat算法(MBA)
选择以从原始数据集中消除不相关的特征。使用简单随机算法修改了Bat算法
采样以从数据集中选择随机实例。排名具有全球最佳功能,可识别
数据集中可用的主要特征。所选要素用于训练随机森林(RF)分类
算法。 MBA特征选择算法提高了RF在识别事件中的分类精度
乳腺癌。威斯康星州诊断乳腺癌数据集(WDBC)用于评估性能
提出的MBA特征选择算法的分析。提出的算法取得了较好的性能。
Kappa统计数据,Mathew的相关系数,精度,F度量,召回率,平均绝对误差(MAE),
均方根误差(RMSE),相对绝对误差(RAE)和均方根误差(RRSE)。

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