One-Class Genetic Algorithm for Authentication Analysis of Spectrochemical Data
José R. de Morais Filho, Camilo de L. M. de Morais, Anne B. F. Câmara, Kássio M. G. Lima
TL;DR
This paper introduces a new method combining genetic algorithms with one-class classifiers to improve disease diagnosis using spectrochemical data.
Contribution
A novel one-class genetic algorithm (OGA) is proposed for variable selection in one-class classification of clinical spectrochemical data.
Findings
The OGA-PRM-OCPLS model achieved 100% sensitivity for COVID-19 and endometriosis classification.
The OGA-DD-SIMCA model achieved 100% sensitivity for dengue classification.
OGA-selected variables can be linked to biomarkers for disease diagnosis.
Abstract
One-class classifier (OCC) models are widely applied to solve classification problems where control or class modeling from a target class is necessary. In this study, OCC models such as Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) were associated with a new variable selection strategy, the one-class genetic algorithm (OGA), for classification analyses in three clinical applications: COVID-19, endometriosis, and dengue samples. DD-SIMCA was implemented in a rigorous approach, using α = 0.05, while OCPLS was performed with partial robust M-regression (PRM). For the three cases, a better classification performance was obtained using the OGA associated with the OCC model. The performance of the OGA-PRM-OCPLS showed better results for both COVID-19 and endometriosis cases when compared to DD-SIMCA, with a classification…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMosquito-borne diseases and control · Identification and Quantification in Food · SARS-CoV-2 detection and testing