Multilabel Prediction for Primary Tumor Surgery Classification Using Logistic Regression with One-vs-One and One-Against-One Approaches
Abstract
In the field of medical data analysis, the accurate classification of primary tumor surgeries is of paramount importance for diagnosing and treating patients effectively. In this study, we explore the application of Logistic Regression with two different multilabel strategies, namely one-vs-one and one-against-one, to predict primary tumor surgery outcomes using the Primary Tumor Surgery dataset. This dataset consists of 339 data samples with 18 features and 21 distinct classes. Our experiments reveal promising results, with the one-vs-one approach achieving an accuracy of 93.67%, precision of 93.7%, and recall of 93.7%, while the one-against-one approach attained an accuracy of 92.45%, precision of 92.4%, and recall of 92.5%. This research not only highlights the effectiveness of Logistic Regression for multilabel prediction in the medical domain but also emphasizes the significance of choosing an appropriate multilabel strategy for optimizing classification performance. We discuss the implications of our findings and potential applications in improving patient care.
Keywords
Download Options
Introduction
Malignant growth has been described as a heterogeneous illness comprising of various subtypes. The early determination and visualization of a disease type have turned into a need in malignant growth research, as it can work with the resulting clinical administration of patients [3] [8]. The significance of arranging malignant growth patients into high or generally safe gatherings has driven many exploration groups, from the biomedical and the bioinformatics field, to concentrate on the utilization of AI (ML) techniques. Hence, these procedures have been used as a mean to demonstrate the movement and therapy of malignant circumstances.
With the approach of new advances in the field of medication, a lot of malignant growth information have been gathered and are accessible to the clinical examination local area. Nonetheless, the exact expectation of an illness result is one of the most intriguing and testing errands for doctors. Thus, ML strategies have turned into a well known instrument for clinical specialists. These methods can find and distinguish examples and connections between them, from complex datasets, while they can really foresee future results of a malignant growth type.
Conclusion
In conclusion, this research demonstrates the effectiveness of Logistic Regression for multilabel prediction in the context of primary tumor surgery classification. The choice of multilabel strategy can influence model performance, and careful consideration of the dataset and task is essential. These findings contribute to the ongoing efforts to leverage machine learning for enhancing patient care and medical decision-making in the field of oncology.
Further research can explore the incorporation of additional features or the utilization of more complex machine learning algorithms to improve predictive accuracy. Moreover, exploring the generalizability of these findings on larger and more diverse medical datasets is crucial.