A natural foundation for disease models is physiological knowledge, as such knowledge facilitates building clinically understandable models.
In contrast to ANOVA, GEMANOVA is especially suited for modeling complex interactions and making easily understandable models of these.
Such technologies require an adequate methodological support so that well-structured and easily understandable models can be constructed.
Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models.
The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown.
QSAR modelling methods cover a wide range of approaches and address many user model building requirements, since they include regression and classification algorithms, eager and lazy approaches, and algorithms producing more easily interpretable and understandable models.
Data Mining is the process of automatic discovery of novel and understandable models and patterns from large amounts of data.
EbCA methodology fits into the general framework of KDD where both the prior knowledge and the post-processing of results are as important as the analysis itself to produce useful and understandable models [ 46, 53].
Semantic interoperability is a crucial element to make building information models understandable and model data sharable across multiple design disciplines and heterogeneous computer systems.
