Enhanced Hepatitis B Diagnosis: Analysis of Intelligent Systems Utilizing Fuzzy Logic

Hepatitis B is a prevalent viral infection that affects millions of people globally. It presents substantial health hazards and necessitates precise diagnostic techniques for successful treatment. This research provides a detailed investigation into the creation and application of intelligent hybrid systems to improve the diagnosis of hepatitis B. These systems incorporate fuzzy logic as a crucial element. By utilizing a wide range of data sources and implementing advanced algorithms, our technique strives to enhance the precision and effectiveness of diagnostic processes. By conducting rigorous experimentation and analysis on an extensive dataset, our proposed technique attains a remarkable accuracy rate of 94.35%. This accomplishment signifies a significant progress in the diagnosis of hepatitis B, surpassing current diagnostic methods. Our methodology exceeds the accuracy reported in the recent and highly regarded article by Dalwinder Singh et al., which reached an accuracy of 94%. In addition, our work explores the complexities of incorporating fuzzy logic into intelligent systems, clarifying its function in improving diagnostic accuracy. Our solution utilizes fuzzy logic to effectively handle the ambiguity and unpredictability in hepatitis B diagnostic data by using its inherent flexibility and adaptability. The ramifications of our findings go beyond only diagnosis accuracy, potentially transforming hepatitis B management techniques. Our methodology enables the early and accurate detection of conditions, which can result in prompt interventions, enhanced patient outcomes, and more efficient public health initiatives.