Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal MEDALION JOURNAL: Medical Research, Nursing, Health and Midwife Participation
Ghialti Novilia
Unknown Affiliation
Dentistry Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
WEB-BASED MEDICAL EXPERT SYSTEM FOR DIAGNOSING KIDNEY DISEASES: THE SYSTEMATIC LITERATURE REVIEW Ghialti Novilia; Muhammad Kahfi Aulia; Eka Utaminingsih
MEDALION JOURNAL: Medical Research, Nursing, Health and Midwife Participation Vol. 5 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/medalion.v5i3.148

Abstract

The kidneys play a vital role in filtering toxins from the blood, producing hormones, and maintaining metabolic processes in the body. However, kidney diseases, including kidney stones, kidney infections, and kidney failure, are increasingly prevalent due to poor lifestyle choices. Expert systems, designed to simulate the decision-making capabilities of specialists, are gaining prominence in diagnosing various diseases, including kidney disorders. This study employs a Systematic Literature Review (SLR) methodology to analyze eight recent articles on web-based expert systems for kidney disease diagnosis. The review examines various expert system approaches, including Certainty Factor, Dempster Shafer, and Forward Chaining, and their effectiveness in diagnosing conditions like kidney stones, acute kidney failure, and chronic kidney disease. The results reveal that while these systems offer reliable diagnoses and are accessible across multiple platforms, there is a need for further research in expanding the range of diagnosable diseases and refining diagnostic criteria. Suggestions for future work include incorporating user-specific data, such as gender and lifestyle, and exploring alternative diagnostic approaches like fuzzy logic systems. This study contributes valuable insights into the development of more comprehensive and accessible web-based expert systems for kidney disease diagnosis, which could support healthcare practitioners and patients alike in the timely detection and management of kidney-related conditions.
Co-Authors Muhammad Kahfi Aulia Utaminingsih, Eka