Tabatabai Mohammad, Wilus Derek, Chen Chau-Kuang, Singh Karan P, Wallace Tim L
School of Global Health, Meharry Medical College, Nashville, TN 37208, USA.
School of Medicine, University of Texas at Tyler, Tyler, TX 75708, USA.
Bioengineering (Basel). 2024 Dec 24;12(1):2. doi: 10.3390/bioengineering12010002.
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.
机器学习的分类方法已在几乎每个学科中广泛使用。一种名为塔巴回归的新分类方法被引入用于分析二元、多项和有序结果。为了评估塔巴回归的性能，分析了从梅奥诊所研究中获得的肝硬化数据。然后将结果与人工神经网络（ANN）、随机森林（RF）、逻辑回归（LR）和概率分析（PA）进行比较。使用肝硬化数据的结果表明，基于真阳性率、F分数、准确率和受试者工作特征曲线下面积（AUC），塔巴回归模型可能是其他分类模型的竞争对手。研究人员和从业者可以将塔巴回归用作机器学习中的一种替代分类方法。总之，当应用于肝硬化数据时，塔巴回归在准确率、召回率、F分数和AUC方面提供了可靠的结果。
