Method of thromboembolism prediction in advanced atherosclerosis

Abstract

Aim. To develop a method for thromboembolism prediction in patients with ad­vanced atherosclerosis.Material and methods. The study involved 170 patients with advanced atheroscle­rosis and 110 patients with local atherosclerotic lesions. At the first stage, speciali­zed experts generated signs characterizing the severity of prothrombotic conditions. To determine whether patients belonged to the first or second group, the multivariate statistics method was used. Decision rules (DRs) was developed based on soft computing (SC), as well as the methodology for the synthesis of hybrid fuzzy DRs.Results. According to the discriminant function coefficient (DFC), the division into classes was made with the formation of an intersection area, which affected the diagnostic sensitivity of this method (=86%). Due to difficulties of separating more heterogeneous groups with a small sample, RP was synthesized according to the atherosclerosis severity classification using SC technology. Atherosclerosis severity classification was adopted according to fuzzy DRs: initiation stage (up to 20%); reverse stage (21-40%); progressive stage (41-65%); critical stage (more than 65%). As­ses­sment of thromboembolism risk in advanced atherosclerosis in the examined pati­ents showed the following trends: progressive stage — 55% of patients; critical stage — 21% of patients; reverse stage — 16% of patients; initiation stage — 7% of patients.Conclusion. DFC for DRs makes it possible to identify groups with high and low thromboembolism risk. The resulting final fuzzy DRs make it possible to differentiate the atherothrombotic condition in advanced atherosclerosis by severity, which can help to timely determine preventive and therapeutic measures.

Keywords

• thromboembolism
• advanced atherosclerosis
• fuzzy decision logic
• membership function
• informative features