Affiliation:
1. Institute of Information Technologies, Federal State Budget Educational Institution of Higher Education, MIREA—Russian Technological University, 78, Vernadsky Avenue, 119454 Moscow, Russia
Abstract
This paper discusses the problem of detecting cancer using such biomarkers as blood protein markers. The purpose of this research is to propose an approach for making decisions in the diagnosis of cancer through the creation of cost-sensitive SVM classifiers on the basis of datasets with a variety of features of different nature. Such datasets may include compositions of known features corresponding to blood protein markers and new features constructed using methods for calculating entropy and fractal dimensions, as well as using the UMAP algorithm. Based on these datasets, multiclass SVM classifiers were developed. They use cost-sensitive learning principles to overcome the class imbalance problem, which is typical for medical datasets. When implementing the UMAP algorithm, various variants of the loss function were considered. This was performed in order to select those that provide the formation of such new features that ultimately allow us to develop the best cost-sensitive SVM classifiers in terms of maximizing the mean value of the metric MacroF1−score. The experimental results proved the possibility of applying the UMAP algorithm, approximate entropy and, in addition, Higuchi and Katz fractal dimensions to construct new features using blood protein markers. It turned out that when working with the UMAP algorithm, the most promising is the application of a loss function on the basis of fuzzy cross-entropy, and the least promising is the application of a loss function on the basis of intuitionistic fuzzy cross-entropy. Augmentation of the original dataset with either features on the basis of the UMAP algorithm, features on the basis of the UMAP algorithm and approximate entropy, or features on the basis of approximate entropy provided the creation of the three best cost-sensitive SVM classifiers with mean values of the metric MacroF1−score increased by 5.359%, 5.245% and 4.675%, respectively, compared to the mean values of this metric in the case when only the original dataset was utilized for creating the base SVM classifier (without performing any manipulations to overcome the class imbalance problem, and also without introducing new features).
Reference97 articles.
1. (2024, January 04). 2021 Global Health Care Outlook. Available online: https://www2.deloitte.com/cn/en/pages/life-sciences-and-healthcare/articles/2021-global-healthcare-outlook.html.
2. Conceptual innovation: 4P Medicine and 4P surgery;Slim;J. Visc. Surg.,2021
3. Development of diagnostic and prognostic molecular biomarkers in hepatocellular carcinoma using machine learning: A systematic review;Brar;Liver Cancer Int.,2022
4. Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer;Li;Chem. Sci.,2023
5. Ma, J., Bo, Z., Zhao, Z., Yang, J., Yang, Y., Li, H., Yang, Y., Wang, J., Su, Q., and Wang, J. (2023). Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma. Cancers, 15.