Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets.

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models.

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.

Background: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.

Objective: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers.

Methods: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS.