Overview of a Ml-Assisted Multimarker Biosensing System (IMAGE)
Caption
The set of sensing signals collected for each patient were then analyzed using ML to screen the patient for PCa. Seventy-six urine samples were measured three times, thereby generating 912 biomarker signals or 228 sets of sensing signals. We used RF and NN algorithms to analyze the multimarker signals. Both algorithms provided an increased accuracy, and the AUROC increased in size as the number of biomarkers was increased.
Credit
Korea Institute of Science and Technology(KIST)
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