We present a system called Risk-O-Meter to predict and analyze clinical risk via data imputation, visualization, predictive modeling, and association rule exploration. Clinical risk calculators provide information about a person's chance of having a disease or encountering a clinical event. Such tools could be highly useful to educate patients to understand and monitor their health conditions. Unlike existing risk calculators that are primarily designed for domain experts, Risk- O-Meter is useful to patients who are unfamiliar with medical terminologies, or providers who have limited information about a patient. Risk-O-Meter is designed in a way such that it is exible enough to accept limited or incomplete data in- puts, and still manages to predict the clinical risk efficiently and effiectively. Current version of Risk-O-Meter evaluates 30-day risk of hospital readmission. However, the proposed system framework is applicable to general clinical risk pre- dictions. In this demonstration paper, we describe different components of Risk-O-Meter and the intelligent algorithms associated with each of these components to evaluate risk of readmission using incomplete patient data inputs.