Minimizing False Negatives of Measles Prediction Model: An Experimentation of Feature Selection Based On Domain Knowledge and Random Forest Classifier

Abstract

In the context of disease prediction model, false negative error occurs when the patient is wrongly predicted as free from the disease.A prediction model development involves the process of data collection and feature selection which extracts relevant features from the dataset. Two commonly employed feature selection approaches are domain knowledge and datadriven, that suffer from bias towards past or current knowledge when applied alone.In this research, we have studied the developmentof measles prediction model by incorporating both the domain knowledge and the data-driven approaches, in particular, the Random Forest classifier.The domain expert has earlier on set the important features based uponhisprior knowledgeon measles for the purpose of minimizing the size of features. Afterward, the attributes became the input in Random Forest classifier and the least important attributes are excluded using the Mean Decrease Gini, in order to experiment its effect on the result. It is found that the removal ofseveral attributes after domain knowledge consultation can provide a good model with less false negative errors.

Evaluating Measles Incidence Rates Using Machine Learning and Time Series Methods in the Center of Iran, 1997-2020

“…In our study, the cross-validation technique was carried out over hundreds of repetitions to estimate the measles outcome. Machine learning and stochastic process approaches are widely used in different aspects such as RF for minimizing false negatives of measles prediction model ( 26 ) and zero-inflated models to investigate the transmission of measles ( 27 ).…”