Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Journal of Advanced Health Informatics Research
Burhanuddin bin Mohd Aboobaider
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparison of Classification and Regression Model Approaches on the Main Causes of Stroke with Symbolic Regression Feyn Qlattice Purwono, Purwono; Agung Budi Prasetio; Burhanuddin bin Mohd Aboobaider
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.87

Abstract

Stroke is one of the deadliest diseases in the world, caused by damage to brain tissue resulting from a blockage in the cerebrovascular system. Proper treatment is essential to avoid worsening complications in patients. Several main triggering factors for stroke include hypertension, obesity, smoking habits, lack of physical activity, excessive alcohol consumption, diabetes, and high cholesterol levels. The advancement of information technology allows for early disease prediction through the utilization of AI and Machine Learning technology. The vast amount of data available on medical and health services worldwide can be maximized to identify risk factors for various diseases, including stroke. Machine learning techniques can be employed to predict the causes of stroke. In this study, we were inspired to use the Feyn Qlattice model approach to address stroke. Both classification and regression models were tested in this study. The results indicate that the classification model performs better, achieving an accuracy rate of 0.95. In contrast, the regression model yielded less satisfactory results, with R2, MAE, and RMSE values considered inadequate. This conclusion is supported by the regression plot and residual plot, both of which indicate suboptimal performance. Hence, maximizing the use of the Feyn Qlattice regression model in datasets related to the causes of stroke is recommended
Understanding the Role of Artificial Intelligence in Community and Home Nursing Care: A Systematic Literature Review Sony Kartika Wibisono; Oktavia Putri Handayani; Burhanuddin bin Mohd Aboobaider
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2225

Abstract

Community and home nursing care are increasingly central to health systems in response to population ageing, rising chronic disease burden, and the need to reduce avoidable hospital utilization. Artificial intelligence (AI) has emerged as a technological innovation with potential to support nursing practice in non-hospital settings. However, the role and implications of AI within community and home nursing care have not been systematically synthesized. This systematic literature review aimed to examine how AI supports community and home nursing practice, identify the types of AI technologies applied, and analyze their reported outcomes and implications for nursing care. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 237 records were identified through electronic database searches. After duplicate removal and screening, 38 full-text articles were assessed for eligibility, and 15 studies were included in the final qualitative synthesis. The included studies, published between 2024 and 2026, encompassed diverse methodological designs and were conducted in community-based, home health, telemonitoring, and mobile nursing contexts. The findings indicate that AI technologies primarily include machine learning–based predictive models, clinical decision support systems, telemonitoring platforms, digital wound assessment tools, and large language model–supported analytics are used to enhance risk prediction, remote monitoring, chronic disease management, and care coordination. Across studies, AI was associated with improved early detection of clinical deterioration, enhanced workflow efficiency, and potential reductions in hospital admissions. Nevertheless, effective implementation depended on nurse engagement, system usability, digital literacy, and organizational support. AI demonstrates substantial potential to strengthen community and home nursing care when integrated within a human-centered and ethically grounded framework that preserves professional nursing judgment.
Co-Authors Agung Budi Prasetio Oktavia Putri Handayani Purwono, Purwono Sony Kartika Wibisono