Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Background: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

Methods: A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled.

Artificial neural networks-based classification of emotions using wristband heart rate monitor data.

Heart rate variability (HRV) is an objective measure of emotional regulation. This study aimed to estimate the accuracy with which an artificial neural network (ANN) algorithm could classify emotions using HRV data that were obtained using wristband heart rate monitors.Four emotions were evoked during gameplay: pleasure, happiness, fear, and anger.

A pervasive health monitoring service system based on ubiquitous network technology.

Objectives: The phenomenon of aging society has derived problems such as shortage of medical resources and reduction of quality in healthcare services.

Method: This paper presents a system infrastructure for pervasive and long-term healthcare applications, i.e.

A mobile care system with alert mechanism.

Hypertension and arrhythmia are chronic diseases, which can be effectively prevented and controlled only if the physiological parameters of the patient are constantly monitored, along with the full support of the health education and professional medical care. In this paper, a role-based intelligent mobile care system with alert mechanism in chronic care environment is proposed and implemented. The roles in our system include patients, physicians, nurses, and healthcare providers.