Using KMeans Clustering to Evaluate and Alert for Deviations of Linac Photon Beam Parameters.

Objective: To analyse the daily measured Dosimetric Quality Assurance (QA) parameters of linear accelerator (linac) using Unsupervised Machine Learning (ML) Algorithm thereby evaluating the current machine status and to highlight the probable cause of the 'out-of-range' measured parameter.

Methods: Five Parameters measured using PTW QuickCheckwebline device in a linac is subjected to KMeans clustering technique.  The measured parameters comprise of Central Axis Dose (CAX), Beam Flatness, SymmetryLR, SymmetryGT and Beam Quality (BQF).