This project leverages data from 382 thyroid cancer patients over 15 years to develop a model to predict propensity for reoccurrence based on 16 base features. This work extends and further explores model improvements off the work done and published by Unviversity of California Irvine (see citation below). This project using the same base data, an acnhor model (KNN) to baseline vs. the UCI work but then other model types with the aim of training a classification model with better performance scores - and save more lives
Original study: Borzooei,Shiva and Tarokhian,Aidin. (2023). Differentiated Thyroid Cancer Recurrence. UCI Machine Learning Repository. https://doi.org/10.24432/C5632J.