一种检测糖尿病视网膜病变的有效集成机器学习方法

IF 1.1 Q3 COMPUTER SCIENCE, THEORY & METHODS Open Computer Science Pub Date : 2022-01-01 DOI:10.1515/comp-2020-0222
Penikalapati Pragathi, Agastyaraju Nagaraja Rao
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引用次数: 3

摘要

全世界有数百万人患有糖尿病视网膜病变。这种疾病主要影响眼睛的视网膜,如果没有事先发现,会导致永久性失明。因此,早期发现糖尿病视网膜病变对于保护人们免于失明是非常重要的。在UCI机器学习存储库中的糖尿病视网膜病变数据集上实现了几种机器学习(ML)算法,以检测糖尿病视网膜病变的症状。但是,大多数算法都是单独实现的。因此,本文提出了一种有效的集成ML方法,该方法使用支持向量机(SVM)、主成分分析(PCA)和蛾焰优化技术。首先,将ML算法决策树(DT)、支持向量机(SVM)、随机森林(RF)和Naïve贝叶斯(NB)应用于糖尿病视网膜病变数据集。其中,SVM算法以76.96%的平均性能优于SVM算法。然后,通过集成PCA技术来实现上述所有ML算法,以降低数据集的维数。在对PCA进行整合后,发现NB、RF和SVM算法的性能显著降低;相反,DT的性能有所提高。为了提高机器学习算法的性能,将蛾焰优化技术与支持向量机和主成分分析相结合。在所有考虑的ML算法中,该方法的平均性能为85.61%,并且正确地实现了类标签的分类。
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An effective integrated machine learning approach for detecting diabetic retinopathy
Abstract Millions of people across the world are suffering from diabetic retinopathy. This disease majorly affects the retina of the eye, and if not identified priorly causes permanent blindness. Hence, detecting diabetic retinopathy at an early stage is very important to safeguard people from blindness. Several machine learning (ML) algorithms are implemented on the dataset of diabetic retinopathy available in the UCI ML repository to detect the symptoms of diabetic retinopathy. But, most of those algorithms are implemented individually. Hence, this article proposes an effective integrated ML approach that uses the support vector machine (SVM), principal component analysis (PCA), and moth-flame optimization techniques. Initially, the ML algorithms decision tree (DT), SVM, random forest (RF), and Naïve Bayes (NB) are applied to the diabetic retinopathy dataset. Among these, the SVM algorithm is outperformed with an average of 76.96% performance. Later, all the aforementioned ML algorithms are implemented by integrating the PCA technique to reduce the dimensions of the dataset. After integrating PCA, it is noticed that the performance of the algorithms NB, RF, and SVM is reduced dramatically; on the contrary, the performance of DT is increased. To improve the performance of ML algorithms, the moth-flame optimization technique is integrated with SVM and PCA. This proposed approach is outperformed with an average of 85.61% performance among all the other considered ML algorithms, and the classification of class labels is achieved correctly.
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来源期刊
Open Computer Science
Open Computer Science COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
4.00
自引率
0.00%
发文量
24
审稿时长
25 weeks
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