A Dynamic Fuzzy Rule-Based Inference System: Fuzzy Inference with Semantic Reasoning

Abstract

The challenge of making flexible, standard, and early medical diagnoses is significant. It has some limitations that it hasn't fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress. Avoid isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemical, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently. It has a great relationship with semantics, varies widely, and has linguistic information. Symptoms cannot be ignored or fixed with variables with specific values. Negatively affects early diagnosis decision-making. Under the circumstances, the diagnosis is made solo on imaging and various medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer's disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN, respectively.