In this section, it will be explored solution taken into consideration the feature analysis above. The intention of exploiting machine learning in breast shape prediction is to estimate the point coordinates after surgery healing, taking as input the points positions before surgery, as well as breast and tumor characteristics. Since the points coordinates are continuous variables in 3D space, learning techniques providing regression methodologies are taken into consideration; however, prediction of the points coordinates poses a challenge to transfer breasts with different laterality or size, into the same coordinate system. Such circumstance can be prevented by re-formulating the demanded output from the regression. Instead of predicting the exact point coordinate, required displacement to translate a pre-surgery point to its post-surgery location can be predicted, alternatively. The post-surgery PCL is then attainable by applying predicted displacement on the pre-surgery PCL. Described in mathematical notation, the regression model can be expressed as in Equation (1):(1)PpreF⋮⋮→fdisppre→post⋮, where Ppre is pre-surgery PCL, F is the feature list per instances (pre-surgery points), f is the demanded regression model, and finally disppre→post expresses the required displacements to convert pre-surgery PCL to post-surgery. Having predicted the displacement, predicted breast shape (Ppred) is attainable via Equation (2):(2)Ppre+disppre→post=Ppred