Ye In Jane Hwang, Amanuel Hagos, Adrienne Withall, Stephen Hampton, Phillip Snoyman, Tony Butler
Population ageing, incarceration and the growing digital divide: Understanding the effects of digital literacy inequity experienced by older people leaving prison
PloS one
alzheimers disease ad progressive neurodegenerative disorder characterized cognitive decline memory impairments behavioral changes presence abnormal betaamyloid plaques tau protein tangles brain known associated ad however current limitations imaging technology hinder direct detection substances consequently researchers exploring alternative approaches indirect assessments involving monitoring brain signals cognitive decline levels blood biomarkers recent studies highlighted potential integrating genetic information approaches enhance early detection diagnosis offering comprehensive understanding ad pathology beyond constraints existing imaging methods study utilized electroencephalography eeg signals genotypes polygenic risk scores prss features machine learning models compared performance gradient boosting xgb random forest rf support vector machine svm determine optimal model statistical analysis revealed significant correlations eeg signals clinical manifestations demonstrating ability distinguish complexity ad diseases using genetic information integrating eeg genetic data svm model achieved exceptional classification performance accuracy 0920 area curve 0916 study presents novel approach utilizing realtime eeg data genetic background information multimodal machine learning experimental results validate effectiveness concept providing deeper insights actual condition patients ad overcoming limitations associated singleoriented data