Intelligent Medical Diagnosis Using Rule-Based Expert System and Deep Belief Networks

Abstract

Getting a precise diagnosis is vital for decent healthcare, yet most clinical expert systems and solo deep learning models encounter problems with either complex patient information or transparency. A solution is proposed here, where a system uses expert logic and deep belief networks to help with medical decisions. It uses any rigidly designed inference standards alongside deep neural layers to better explain and predict results. A set of medical data from different diseases was included in the system's training and evaluation. This model proved accurate, with 95.2% results, a sensitivity of 93.1%, a specificity of 94.4%, and an F1-score of 92.6%. It outperformed standalone models and last year's baselines by 4-7% in all measured areas. Comparative results show that inference time drops and the model's reliability improves. The presented approach is flexible and easy to understand, improving clinical judgment, and is useful in different medical branches.

Ähnliche Arbeiten