Predictive modelling typically involves several steps: data collection, data preprocessing, model selection, training, validation, and deployment. Histological data, often in the form of digital images or quantifiable markers, is collected and preprocessed to remove noise and normalize the data. Suitable models are then selected and trained using this data to make predictions about new, unseen samples.
