Automatically Finding the Biggest Fold Value for More Accurate Classification and Diagnosis in Machine Learning Algorithms

IF 1.5 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Iranian Journal of Science and Technology-Transactions of Electrical Engineering Pub Date : 2023-12-18 DOI:10.1007/s40998-023-00682-x
Emre Avuçlu
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Abstract

Correct diagnosis in medicine is of great importance as it is one of the most important issues in medicine. Today, researchers have embarked on many new searches to make an accurate medical diagnosis. In order for any disease to be cured, it is necessary to define it precisely early and accurately. In this study, a new method was proposed to make a more accurate medical diagnosis. This method is based on automatically selecting the fold with the best accuracy rate after k-fold crossvalidation is performed in any database. In this way, scientific studies that lead to more accurate results will be carried out by using the fold with the highest accuracy in both classification and medical diagnosis procedures. This method has been applied on two different databases, Ecoli and Wisconsin Breast Cancer Diagnostic (WBCD) databases, which are used in scientific studies by many researchers in the literature. The statistical measurements of each fold values of both databases used have been examined in detail. Diagnostics for these databases were carried out using 7 different Machine Learning Algorithms (MLA), (k nearest neighbor (k-NN), Decision Tree (DT), Random Forest (RF), Multinominal Logistic Regression (MLR), Naive Bayes (NB), Support Vector Machine (SVM), Minumum (Mean) Distance Classifier (MMDC)). In the test procedures for Ecoli dataset, the following accuracy values were obtained for k-NN, DT, RF, MLR, NB, SVM, MMDC, respectively; 0.8485, 0.8358, 0.9848, 0.8182, 0.6667, 0.8636, 0.7424. For the WBCD database, the following accuracy values were obtained for k-NN, DT, RF, MLR, NB, SVM, MMDC, respectively; 0.9856, 0.9568, 0.9784, 0.9856, 0.9856, 0.9856, 0.9784. Other results were given in detail in the experimental studies section. It is of great importance to choose the most accurate MLAs to be used in medical diagnosis for human life. Thus, in the studies to be done with MLAs in medicine or any field in the literature, how the best score that can be obtained from MLAs will be introduced to the literature. In this study, an original study was conducted on how to make the correct medical diagnosis, which is one of the most important issues for human life.

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在机器学习算法中自动寻找最大折叠值以实现更准确的分类和诊断
正确的医学诊断非常重要,因为它是医学中最重要的问题之一。如今,研究人员已经开始了许多新的探索,以做出准确的医学诊断。要想治愈任何疾病,就必须及早准确地界定疾病。在这项研究中,提出了一种新方法来进行更准确的医学诊断。这种方法的基础是在任何数据库中进行 k 倍交叉验证后,自动选择准确率最高的折叠。这样,在分类和医疗诊断程序中使用准确率最高的折叠,就能进行科学研究,得出更准确的结果。该方法已应用于两个不同的数据库,即 Ecoli 和威斯康星乳腺癌诊断(WBCD)数据库,这些数据库在科学研究中被许多研究人员在文献中使用。我们详细研究了这两个数据库中每个折叠值的统计测量结果。对这些数据库的诊断使用了 7 种不同的机器学习算法(MLA)(k 近邻算法(k-NN)、决策树算法(DT)、随机森林算法(RF)、多义逻辑回归算法(MLR)、奈夫贝叶斯算法(NB)、支持向量机算法(SVM)、最小(平均)距离分类器算法(MMDC))。在生态大肠杆菌数据集的测试程序中,k-NN、DT、RF、MLR、NB、SVM、MMDC 的准确率分别为 0.8485、0.8358、0.9848、0.8182、0.6667、0.8636、0.7424。在 WBCD 数据库中,k-NN、DT、RF、MLR、NB、SVM、MMDC 的准确度值分别为 0.9856、0.9568、0.9784、0.9856、0.9856、0.9784。其他结果详见实验研究部分。选择最准确的工作重点用于医学诊断对人类生命至关重要。因此,在医学或任何领域的文献中对 MLA 进行研究时,都会介绍如何从 MLA 中获得最佳分数。本研究对如何做出正确的医学诊断进行了原创性研究,这是人类生活中最重要的问题之一。
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来源期刊
CiteScore
5.50
自引率
4.20%
发文量
93
审稿时长
>12 weeks
期刊介绍: Transactions of Electrical Engineering is to foster the growth of scientific research in all branches of electrical engineering and its related grounds and to provide a medium by means of which the fruits of these researches may be brought to the attentionof the world’s scientific communities. The journal has the focus on the frontier topics in the theoretical, mathematical, numerical, experimental and scientific developments in electrical engineering as well as applications of established techniques to new domains in various electical engineering disciplines such as: Bio electric, Bio mechanics, Bio instrument, Microwaves, Wave Propagation, Communication Theory, Channel Estimation, radar & sonar system, Signal Processing, image processing, Artificial Neural Networks, Data Mining and Machine Learning, Fuzzy Logic and Systems, Fuzzy Control, Optimal & Robust ControlNavigation & Estimation Theory, Power Electronics & Drives, Power Generation & Management The editors will welcome papers from all professors and researchers from universities, research centers, organizations, companies and industries from all over the world in the hope that this will advance the scientific standards of the journal and provide a channel of communication between Iranian Scholars and their colleague in other parts of the world.
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