Thyroid Disease Classification Using Machine Learning Algorithms
| dc.contributor.author | Salman, K. | |
| dc.contributor.author | Sonuc, E. | |
| dc.date.accessioned | 2024-09-29T16:21:03Z | |
| dc.date.available | 2024-09-29T16:21:03Z | |
| dc.date.issued | 2021 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description | 2nd International Conference on Physics and Applied Sciences, ICPAS 2021 -- 26 May 2021 through 27 May 2021 -- Baghdad -- 170835 | en_US |
| dc.description.abstract | With the vast amount of data and information difficult to deal with, especially in the health system, machine learning algorithms and data mining techniques have an important role in dealing with data. In our study, we used machine learning algorithms with thyroid disease. The goal of this study is to categorize thyroid disease into three categories: hyperthyroidism, hypothyroidism, and normal, so we worked on this study using data from Iraqi people, some of whom have an overactive thyroid gland and others who have hypothyroidism, so we used all of the algorithms. Support vector machines, random forest, decision tree, naïve bayes, logistic regression, k-nearest neighbors, multi-layer perceptron (MLP), linear discriminant analysis. To classification of thyroid disease. © Published under licence by IOP Publishing Ltd. | en_US |
| dc.identifier.doi | 10.1088/1742-6596/1963/1/012140 | |
| dc.identifier.issn | 1742-6588 | |
| dc.identifier.issue | 1 | en_US |
| dc.identifier.scopus | 2-s2.0-85112402019 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1088/1742-6596/1963/1/012140 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/9503 | |
| dc.identifier.volume | 1963 | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IOP Publishing Ltd | en_US |
| dc.relation.ispartof | Journal of Physics: Conference Series | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | classification model | en_US |
| dc.subject | Decision tree | en_US |
| dc.subject | K-nearest neighbors | en_US |
| dc.subject | Linear discriminant analysis | en_US |
| dc.subject | logistic regression | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Multi-layer perceptron (MLP) | en_US |
| dc.subject | Naive bayes | en_US |
| dc.subject | Random forest | en_US |
| dc.subject | Support vector machines | en_US |
| dc.subject | Thyroid diseases | en_US |
| dc.title | Thyroid Disease Classification Using Machine Learning Algorithms | en_US |
| dc.type | Conference Object | en_US |
