Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification
The classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/2 |
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| author | Mohammad Tabatabai Derek Wilus Chau-Kuang Chen Karan P. Singh Tim L. Wallace |
| author_facet | Mohammad Tabatabai Derek Wilus Chau-Kuang Chen Karan P. Singh Tim L. Wallace |
| author_sort | Mohammad Tabatabai |
| collection | DOAJ |
| description | The classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained from a Mayo Clinic study were analyzed. The results were then compared with an artificial neural network (ANN), random forest (RF), logistic regression (LR), and probit analysis (PA). The results using cirrhosis data revealed that the Taba regression model could be a competitor to other classification models based on the true positive rate, F-score, accuracy, and area under the receiver operating characteristic curve (AUC). Taba regression can be used by researchers and practitioners as an alternative method of classification in machine learning. In conclusion, the Taba regression provided a reliable result with respect to accuracy, recall, F-score, and AUC when applied to the cirrhosis data. |
| format | Article |
| id | doaj-art-9b06f148349544ba9209063c9746e3a5 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-9b06f148349544ba9209063c9746e3a52025-01-24T13:22:55ZengMDPI AGBioengineering2306-53542024-12-01121210.3390/bioengineering12010002Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for ClassificationMohammad Tabatabai0Derek Wilus1Chau-Kuang Chen2Karan P. Singh3Tim L. Wallace4School of Global Health, Meharry Medical College, Nashville, TN 37208, USASchool of Global Health, Meharry Medical College, Nashville, TN 37208, USASchool of Global Health, Meharry Medical College, Nashville, TN 37208, USASchool of Medicine, University of Texas at Tyler, Tyler, TX 75708, USASchool of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37208, USAThe classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained from a Mayo Clinic study were analyzed. The results were then compared with an artificial neural network (ANN), random forest (RF), logistic regression (LR), and probit analysis (PA). The results using cirrhosis data revealed that the Taba regression model could be a competitor to other classification models based on the true positive rate, F-score, accuracy, and area under the receiver operating characteristic curve (AUC). Taba regression can be used by researchers and practitioners as an alternative method of classification in machine learning. In conclusion, the Taba regression provided a reliable result with respect to accuracy, recall, F-score, and AUC when applied to the cirrhosis data.https://www.mdpi.com/2306-5354/12/1/2artificial neural networkrandom forestlogistic regressionprobit analysismachine learningclassification |
| spellingShingle | Mohammad Tabatabai Derek Wilus Chau-Kuang Chen Karan P. Singh Tim L. Wallace Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification Bioengineering artificial neural network random forest logistic regression probit analysis machine learning classification |
| title | Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification |
| title_full | Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification |
| title_fullStr | Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification |
| title_full_unstemmed | Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification |
| title_short | Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification |
| title_sort | taba binary multinomial and ordinal regression models new machine learning methods for classification |
| topic | artificial neural network random forest logistic regression probit analysis machine learning classification |
| url | https://www.mdpi.com/2306-5354/12/1/2 |
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