Fuzzy Logic for Risk Assessment and Early Disease Prediction in Healthcare

Initially, the medical diagnosis and illness forecasting gets more complicated. Thus, it is necessary to have advanced systems for making decisions that can handle uncertainty and lack of information. The role of expert-driven models in the healthcare sector, highlighting their ability to enhance risk assessment and promote early disease detection. In opposition to traditional statistical and machine learning techniques, these mathematical models blend expert knowledge with computational reasoning, facilitating making decisions that are adaptable, interpretable, and adaptable. The main benefits are that these experts are capable of looking at ambiguous or unreliable medical data, facilitate approximate thinking, and make diagnoses more accurate. A comparative analysis indicates that while machine learning models achieve high accuracy, they ought to often lack transparency, which makes them difficult to interpret in clinical settings. On the other however, expert systems provide a more straightforward framework. Therefore, being suitable for medical professionals seeking reliable and comprehensible support for decision tools. The findings demonstrate that the integration of these technologies into healthcare could significantly improve disease prevention and patient outcomes via early detection and timely interventions. Incorporating real-time processes for learning, strengthening rule-based frameworks, and addressing ethical concerns to enable widespread adoption in medical practice.