  Primary Hyperparathyroidism(PHPT) is a relatively common disease, affecting
about one in every 1,000 adults. However, screening for PHPT can be difficult,
meaning it often goes undiagnosed for long periods of time. While looking at
specific blood test results independently can help indicate whether a patient
has PHPT, often these blood result levels can all be within their respective
normal ranges despite the patient having PHPT. Based on the clinic data from
the real world, in this work, we propose a novel approach to screening PHPT
with neural network (NN) architecture, achieving over 97\% accuracy with common
blood values as inputs. Further, we propose a second model achieving over 99\%
accuracy with additional lab test values as inputs. Moreover, compared to
traditional PHPT screening methods, our NN models can reduce the false
negatives of traditional screening methods by 99\%.
