The diagnostic categories used to define learning disability are not standardized, and categorization systems are vague. This study aimed to explore the diagnostic methodology and strategies used to identify learning disabilities. The aim is to identify abductions in diagnostics in the field of special education. Interpreting diagnostics in remedial education using abduction can help identify learning disabilities more accurately. In the previous research phase, we conducted a meta-analysis of 11 expert reviews to identify abduction using fuzzy logic, fsQCA, and Boolean algebra. This study allowed for the creation of a new abductive diagnostic model. Based on these results, the reliability of the diagnostic process can be increased, and the diagnostic model can be used to detect learning disabilities or other types of problems and to identify sufficient conditions underlying a given phenomenon. Neither qualitative content analysis nor fsQCA revealed a relationship between all variables at a sufficient depth. Thus, in the present study, we moved on to Bayesian meshes, which shift and attempt to reorder previously identified variables based on conditional probability. We hypothesized that the Bayesian mesh and abduction application together may already be an efficient tool, which also anticipates the possibility of automation.