As the complexity of commercial cellular networks grows, there is an increasing need for automated methods detecting and diagnosing cells not only in complete outage but with degraded performance as well. Root cause analysis of the detected anomalies can be tedious and currently carried out mostly manually if at all; in most practical cases, operators simply reset problematic cells. In this paper, a novel integrated detection and diagnosis framework is presented that can identify anomalies and find the most probable root cause of not only severe problems but even smaller degradations as well. Detecting an anomaly is based on monitoring radio measurements and other performance indicators and comparing them to their usual behavior captured by profiles, which are also automatically built without the need for thresholding or manual calibration. Diagnosis is based on reports of previous fault cases by identifying and learning their characteristic impact on different performance indicators. The designed framework has been evaluated with proof-of-concept simulations including artificial faults in an LTE system. Results show the feasibility of the framework for providing the correct root cause of anomalies and possibly ranking the problems by their severity.