Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion

• Published in: Computers in biology and medicine Vol. 150; p. 106181
• Main Authors: Wang, Guowei, Guo, Shuli, Han, Lina, Song, Xiaowei, Zhao, Yuanyuan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2022; Elsevier Limited
• Subjects: Algorithms; Animals; Chemokines; Computed tomography; Conditional probability; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; COVID-19 Testing; Cytokines; D-S evidence theory; Diagnosis; Distribution functions; Domains; Feature extraction; Feature recognition; Humans; Internal Medicine; Medical diagnosis; Multi-modal information; Object recognition; Other; Prediction models; Probability; Probability distribution; Probability distribution functions; Support Vector Machine; Support vector machines; Whale optimization algorithm; Whales; COVID-19; Support vector machine; Whale optimization algorithm; Multi-modal information; D-S evidence theory
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106181

Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases. •CT, routine indexes and cytokines are combined to jointly quantify COVID-19.•An autonomous diagnosis for COVID-19 is proposed based on LWWOA-MSVM.•The angle cosine and Lance distance to solve the high conflict of D-S evidence.