Web-Based Decision Support System for Coronary Heart Disease Diagnosis.

Coronary heart disease is a serious and common disease that affects a large part of the population. There is a tendency to use machine learning techniques for the punctual and valid diagnosis, which can determine the effectiveness of treatment and the health of the individual. In this paper, we apply popular machine learning algorithms, achieving satisfactory performance (over 70%), coinciding with corresponding studies in the literature and at some points overriding them. Moreover, we provide a user-friendly web-based decision support system, which is suitable for specialized medical staff and, based on some easily accessible and available data, contributes to the diagnosis of the disease.

Mobile Application for Monitoring and Preventing Cognitive Decline Through Lifestyle Intervention.

Diagnosing and preventing Alzheimer's disease is a complex task, partly due to being characterized by a lengthy asymptomatic stage. In order to tackle this, most preclinical studies are multidimensional in nature and largely focus on prevention through lifestyle interventions, such as improving nutrition and introducing physical as well as cognitive exercise. With the widespread use of mobile smart devices today, mobile health applications can help inform high-risk individuals at a low cost, while also aiding in the prevention of cognitive decline through constant virtual coaching services that contribute to lifestyle interventions. Under this light, a mobile application is developed in the context of this paper that provides risk assessment of individuals, daily monitoring of factors that have been found to help prevent cognitive impairment, and individually tailored guidance based on the individual's progress. The developed application is also capable of reassessing users' risk to track their progress, while also providing these services in an intuitive and user-friendly manner, which could enable the future development of more accurate models through the collected data.