Systems and methods for preprocessing target data and generating predictions using a machine learning model

A method, the method comprising:

• accessing a machine learning model comprising features that are derived from a training dataset and selected for the machine learning model;
• obtaining target data derived from a target sample;
• normalizing, using prestored model parameters derived from randomly-selected subsets of the training dataset, the target data to generate processed data;
• and
• after generating the processed data using the prestored model parameters, generating, using the machine learning model on the processed data, a likelihood score related to disease occurrence by inputting the processed data into nodes of the machine learning model.

One or more non-transitory machine-readable media storing instructions, that when executed by one or more processing devices, cause operations comprising:

• accessing a machine learning model comprising features that are selected for the machine learning model and derived from a training dataset;
• obtaining target data derived from a target sample;
• generating, using prestored model parameters derived from randomly-selected subsets of the training dataset, processed data from the target data;
• and
• after generating the processed data using the prestored model parameters, generating, using the machine learning model on the processed data, a likelihood value related to disease by inputting the processed data into nodes of the machine learning model.