Browsing by Author "Kishara, J"

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Embargo
Healthy Heart – Heart Risk Prediction System on Personalized Guidance for Heart Patients
(IEEE, 2022-07-18) Bandara, K.R.C; Dureksha, D.D.T.D; Pinidiya, S.C; Amarasinghe, R.M.G.H; Thelijjagoda, S; Kishara, J
Human heart is the principal part of the human body. Change in human lifestyle, work related stress and unhealthy food habits contribute to the increase in rate of numerous heart related diseases. In accordance with several research, various heart diseases have been the key reason for deaths in Sri Lanka. According to the 2018 records, stroke affected 31%, coronary heart disease affected 23%, and ischemic heart disease affected 14%. Therefore, there is a need for an automated system which will enhance medical efficiency and to identify such diseases in time for proper treatment. The proposed system takes physical and medical datasets of heart patients as manual input parameters and predicts the patient’s risk of having a heart disease. Prediction process grants the patient a risk level according to the heart condition and proposes a personalized daily guidance for the patient to avoid risks associated with, along with a meal planner, exercise scheduler and a stress releaser as well as alert the patient well in advance. The system will present an efficient technique of predicting heart diseases using machine learning approaches to analyze huge complex medical data. Some of the used algorithms are Random Forest, Logistic regression, Decision tree classifier etc... The research mainly aims to prevent the escalation of heart diseases in patients and lead them to a healthy lifestyle.
Embargo
Measuring Psychological Stress Rate Using Social Media Posts Engagement
(IEEE, 2022-08-15) Perera, W.T. H; Lanerolle, T. Y; Andrado, Y. D. S; Wickramasinghe, W.A.P.C; Bandara, P.S; Kishara, J
In psychology, stress is a feeling of feelings and pressure. Stress is a type of psychological pain. Literature has showcased that mental health stages like anxiety and depression might be identified by the social media post captions, emojis, and the way users communicate with others. Among the main underlying causes and correlates of illnesses and mental health problems is stress. In this study, we explore the conclusions and posts of psychological stress using the data of social media users, who use and share their Facebook accounts. In the first step, a user who are stressed often post about exhaustion, losing control, increasing self-focus, and physical pain using their post captions, emojis, and post images they usually post on Facebook. Collect and read all the posts that are fetched via the social networks and then measure the stress level against different factors. Then the system demonstrates how the user interacts with the intelligent custom virtual AI counselor application thus innovated can be trained and be scaled to measure against the factors. Data can be collected by using Graph API, followed by machine learning techniques and natural language processing (NLP) techniques, and an intelligent custom AI virtual application to measure stress levels by different factors. Also, use AI techniques to build health guidance plans for everyone with the help of the above collections. And reacting to the simple games is another factor to measure a highly accurate result in stress level. Natural Language Processing (NLP) is commonly used to implement smart communication virtual counselor agents. Scaled social media-based stress measurements outperform survey-based stress measurements, held up against involving a combination of social and demographic factors such as gender, age, race, income, and education. A discussion of the implications of using social media as a new tool for monitoring stress levels and developing health-related advice for individuals is presented in the conclusion.