Primary hyperparathyroidism screening with machine learning

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dc.contributor.advisor Wu, Shaoen
dc.contributor.author Ziems, Noah
dc.date.accessioned 2022-06-15T14:46:29Z
dc.date.available 2022-06-15T14:46:29Z
dc.date.issued 2021-05
dc.identifier.uri http://cardinalscholar.bsu.edu/handle/123456789/203141
dc.description.abstract 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 clinical data from the real world, in this work, we propose a novel approach to screening PHPT with neural network (NN) architectures, 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 can reduce the false negatives of traditional screening methods by 99%. en_US
dc.description.sponsorship Honors College
dc.title Primary hyperparathyroidism screening with machine learning en_US
dc.type Undergraduate senior honors thesis
dc.description.degree Thesis (B.?) en_US


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  • Undergraduate Honors Theses [5928]
    Honors theses submitted to the Honors College by Ball State University undergraduate students in partial fulfillment of degree requirements.

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