Cardiovascular disease, commonly known as heart disease, is one of the leading causes of death in the United States and worldwide. Early detection of the disease can save thousands of lives and billions of dollars in healthcare costs. A statistical model with the ability to accurately predict heart disease could be of immense help to the patients, their families, the medical community, and the healthcare system. Hospitals and providers collect many patient health metrics during screening and routine lab tests, which could be used to build such a statistical model. A robust heart disease prediction model is built using a sample dataset from the University of California, Irvine Machine Learning repository. Initial Hypotheses are formulated, and the most significant predictor variables are identified using the Wald test. The statistical significance of the proposed model is tested using the Likelihood-Ratio test. A repeated 10-fold cross-validation technique is used to evaluate the model's prediction power on previously unseen data. Keeping in mind the simplicity, usability, and explainability of results to the medical community, a Logistic Regression model that predicts the heart disease class with a high degree of accuracy is presented in this paper.