Alzheimer’s Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. Our research explores prediction of AD without medical imaging, in hopes of earlier and cheaper diagnoses. We construct a classification pipeline which shows greater than 90% accuracy and recall in predicting AD with our best model. This model generalizes well to sub-studies of our main data set, ADNI, as well as another AD dataset, AIBL. We also find that we can get close to 79% accuracy with only one clinical visit of data. Finally, we produce a meta-classification algorithm which balances feature cost with accuracy. This work can be adapted into a diagnostic tool for maximizing accuracy while minimizing the amount of tests a patient needs to take for diagnosis.