Automatically Finding the Biggest Fold Value for More Accurate Classification and Diagnosis in Machine Learning Algorithms
| dc.contributor.author | Avuçlu, Emre | |
| dc.date.accessioned | 2024-04-30T10:19:00Z | |
| dc.date.available | 2024-04-30T10:19:00Z | |
| dc.date.issued | 2024 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.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. | |
| dc.identifier.doi | 10.1007/s40998-023-00682-x | |
| dc.identifier.issn | 2228-6179 | |
| dc.identifier.issn | 2364-1827 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https:/dx.doi.org10.1007/s40998-023-00682-x | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/11739 | |
| dc.identifier.wos | WOS:001126755900002 | |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Iranian Journal of Science and Technology | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Biomedical Diagnosis | |
| dc.subject | Biggest k-fold | |
| dc.subject | Cancer Diagnosis | |
| dc.subject | Crossvalidation | |
| dc.subject | Machine Learning Algorithms | |
| dc.title | Automatically Finding the Biggest Fold Value for More Accurate Classification and Diagnosis in Machine Learning Algorithms | |
| dc.type | Article |
