A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature

Abstract

This paper deals with the problem of diagnosing oncological diseases based on blood protein markers. The goal of the study is to develop a novel approach in decision-making on diagnosing oncological diseases based on blood protein markers by generating datasets that include various combinations of features: both known features corresponding to blood protein markers and new features generated with the help of mathematical tools, particularly with the involvement of the non-linear dimensionality reduction algorithm UMAP, formulas for various entropies and fractal dimensions. These datasets were used to develop a group of multiclass kNN and SVM classifiers using oversampling algorithms to solve the problem of class imbalance in the dataset, which is typical for medical diagnostics problems. The results of the experimental studies confirmed the feasibility of using the UMAP algorithm and approximation entropy, as well as Katz and Higuchi fractal dimensions to generate new features based on blood protein markers. Various combinations of these features can be used to expand the set of features from the original dataset in order to improve the quality of the received classification solutions for diagnosing oncological diseases. The best kNN and SVM classifiers were developed based on the original dataset augmented respectively with a feature based on the approximation entropy and features based on the UMAP algorithm and the approximation entropy. At the same time, the average values of the metric MacroF1 - score used to assess the quality of classifiers during cross-validation increased by 16.138% and 7.910%, respectively, compared to the average values of this metric in the case when the original dataset was used in the development of classifiers of the same name.

Applied and Computational Mathematics for Digital Environments

“…A total of 12 papers were submitted to this Special Issue, of which 11 were published (91.67%) [11][12][13][14][15][16][17][18][19][20][21] and only 1 was rejected (8.33%), indicating the very high quality of the original submissions.…”

“…Demidova [21] proposes an approach for diagnosing oncological diseases based on blood protein markers, new features generated using non-linear dimensionality reduction algorithm UMAP, formulas for various entropies and fractal dimensions. The author used resulting datasets with various combinations of features to develop multiclass kNN and SVM classifiers.…”

Decision-Making on the Diagnosis of Oncological Diseases Using Cost-Sensitive SVM Classifiers Based on Datasets with a Variety of Features of Different Natures

“…The logistic regression algorithm [13], k-nearest neighbors (kNN) algorithm [14], support vector machine (SVM) algorithm [15], random forest (RF) algorithm [16] and DL algorithms [8][9][10] are usually applied when creating classifiers to solve OD diagnosis problems. Such classifiers are created on the basis of datasets which accommodate information about both patterns with diagnosed ODs of various types and patterns with unconfirmed ODs (i.e., normal patterns) [13][14][15][16][17][18][19][20].…”

“…It can be reasonably assumed that taking into account the entire spectrum of BPMs should provide an increase in the accuracy of diagnostics of various diseases. The use of data mining (DM) tools with the involvement of ML and DL technologies will allow us to reveal the relationships between the values of BPMs hidden in PT data for different types of ODs [13,14,20].…”

Correction: Demidova, L.A. A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature. Mathematics 2023, 11, 792

“…The author wishes to make the following corrections to this paper [1]: This should be the following: Text Correction 2. The last sentence of the Abstract, the sentence This should be the following: This should be the following: Text Correction 2.…”