With the advancement of relational databases, the number of configuration parameters that control memory allocation, concurrency, cost of query plans, I/O optimization, logging, recovery or transaction consistency, increases. Users and even expert database administrators struggle to tune these parameters in order to ensure high availability and performance, and in many cases rely on their experience and some rules of thumb. Research on improving database manageability has shown that this is a critical, but hard problem. In this paper, we propose a highly accurate multivariate statistical model that identifies databases which are bound to raise high volumes of incidents over time. Moreover, we show that by adding detailed configuration parameters to the model, we can better link the problems reported in incident tickets to specific poor database configurations. Finally, we analyze trends of top-ranked parameters and compare their values between problematic and non-problematic databases, in order to suggest better configurations.