Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis

Felcia Bel, Sabeen Selvaraj
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Abstract

Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method  has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers.
基于物联网的预后医疗数据分析的混合优化特征选择方法
医疗保健是物联网(IoT)中非常重要的应用领域。本研究旨在提供一种新颖的组合特征选择(FS)方法,如基于树的单变量(UV)方法(TB)、基于最小绝对收缩选择算子(LASSO)的递归特征消除(RFE)方法、基于遗传算法(GA)的互信息(MI)方法和基于单变量的嵌入式方法(EM)。适合物联网医疗数据的机器学习算法是逻辑回归(LR)和支持向量机(SVM)。每种组合方法都被应用到机器学习算法中,以找到预后的最佳分类器。计算了逻辑回归和支持向量机的所有组合特征选择方法的各种性能指标后发现,使用递归消除特征选择方法进行精确分类,并将 LASSO 应用于逻辑回归,比所有其他组合方法取得了更好的性能,具有高准确性、高灵敏度和高曲线下面积。数据分析得出的结论是,在比较了所有其他与 LR 和 SVM 分类器相结合的方法后,使用 LR 特征选择方法的 RFE+LASSO 将为基于物联网的心脏病医疗数据集提供更好的整体性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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