Nomogram for a machine learning model with categorical predictors to predict binary outcome

Abstract We proposed a protocol to create a nomogram for a machine learning (ML) prediction model. The applicability is to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This protocol enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. However, this protocol only allows a nomogram creation for a model using categorical predictors to predict a binary outcome. The key stages consisted of providing the input for nomogram creation, and creating and reading the nomogram. This protocol takes 6 to 35 minutes to be completed.