Granular Computing to Forecast Alzheimer's Disease Distinctive Individual Development

Our study aimed to test if clinically tested normal subjects might have patterns in the results of their cognitive tests that have some similarities to suits obtained from patients in different AD stages. Due to the aging population, the prevalence of Alzheimer's Disease (AD) related dementia is fast increasing. That is already a worldwide problem. AD-related neurodegeneration starts several decades before the first symptoms and AD biomarkers were identified in recent years. The purpose of our study was to find AD-related biomarkers in healthy subjects. We have estimated such changes based on the Model consisting of subjects in different AD stages from normal to patients with dementia from Biocard data. By using the granular computing method, we found reducts (sets of attributes) related to different stages of the disease. By applying this classification to psychophysical test results of normal subjects, we have demonstrated if some of them might show some similarities to the first symptoms related to AD. As such psychophysical tests can be easily implemented in computers connected to the internet, our method has the potential to be used as a new AD preventive method. We have analyzed Biocard data by comparing a group of 150 subjects in different AD stages with normal subjects. we have found granules that classify cognitive attributes with disease stages (CDRSUM). By applying these rules to normal (CDRSUM = 0) 21 subjects we have predicted that one subject might get mild dementia (CDRSUM > 4.5), one very mild dementia (CDRSUM > 2.25), and five others might get questionable impairment (CDRSUM > 0.75). AI methods can find, invisible for neuropsychologists, patterns in cognitive attributes of normal subjects that might indicate their pre-dementia stage.