ABSTRACT
BACKGROUND AND OBJECTIVES: A huge number of solutions based on computational systems have been recently developed for the classification of cognitive abnormalities in older people, so that individuals at high risk of developing neurodegenerative diseases, such as Cognitive Impairment and Alzheimer?s disease, can be identified before the manifestation of the diseases. Several factors are related to these pathologies, making the diagnostic process a hard problem to solve. This paper proposes a computational model based on the artificial neural network to classify data patterns of older adults. METHODS: The proposal takes into account the several parameters as diagnostic factors as gender, age, the level of education, study time, and scores from cognitive tests (Mini-Mental State Examination, Semantic Verbal Fluency Test, Clinical Dementia Rating and Ascertaining Dementia). This non-linear regression model is designed to classify healthy and pathological aging with machine learning techniques such as neural networks, random forest, SVM, and stochastic gradient boosting. We deployed a simple linear regression model for the sake of comparison. The primary objective is to use a regression model to analyze the data set aiming to check which parameters are necessary to achieve high accuracy in the diagnosis of neurodegenerative disorders. RESULTS: The analysis demonstrated that the usage of cognitive tests produces median values for the accuracy greater than 90%. The ROC analysis shows that the best sensitivity performance is above 98% and specificity of 96% when the configurations have only cognitive tests. CONCLUSIONS: The presented approach is a valuable tool for identifying patients with dementia or MCI and for supporting the clinician in the diagnostic process, by providing an outstanding support decision tool in the diagnostics of neurodegenerative diseases.
Subject(s)
Cognitive Dysfunction/diagnosis , Dementia/diagnosis , Neural Networks, Computer , Age Factors , Aged , Aged, 80 and over , Early Diagnosis , Female , Humans , Machine Learning , Male , Middle Aged , Nonlinear Dynamics , Prognosis , ROC Curve , Regression Analysis , Sensitivity and Specificity , Sex FactorsABSTRACT
Using optical frequency-domain reflectometry to reveal the gain distribution and allow us to optimize a thulium-doped fiber amplifier, we have demonstrated 18-dB gain by employing only 5 m of a 2000-parts-in-10(6)-Tm-doped fiber pumped with 145 mW of power at dual wavelengths of 800 and 1050 nm. The role of the 800-nm pump, which by itself does not permit population inversion, was clearly observed experimentally.
