HEALTHCARE INFORMATICS WITH FUZZY LOGIC SMARTER DATA, SMARTER DECISIONS

Abstract

  The fast expansion of healthcare data has made sophisticated techniques necessary to extract relevant insights and enable intelligent decision-making in healthcare informatics absolutely important.    A method for computer intelligence, fuzzy logic has recently been quite effective in managing uncertainty, ambiguity, and imprecision in medical data.    The revolutionary possibilities of fuzzy logic in rethinking healthcare informatics, supporting better data analysis, and strengthening decision-making processes are examined in this study.    Healthcare systems may address difficult medical problems, increase diagnosis accuracy, maximize patient treatment regimens, and provide individualized healthcare services by means of fuzzy logic.    The pragmatic uses of fuzzy logic in clinical decision support systems, illness prediction, patient monitoring, and medical diagnostics is reviewed in this work.    It also looks at how newly developing technologies such artificial intelligence (AI), machine learning (ML), Internet of Medical Things (IoMT), and fuzzy logic could be used to thus advance intelligent healthcare solutions.   Furthermore, offering a thorough overview of current fuzzy logic models used in healthcare, the paper assesses their performance and future evolution.     The conclusions underline how crucial fuzzy logic is to enhance healthcare informatics, enable better data utilization, and enable healthcare professionals to make intelligent decisions for improved patient outcomes.   Emphasizing the need of greater research and development in fuzzy logic-driven healthcare informatics to actualize a more efficient, intelligent, and patient-centric healthcare environment, the paper concluded.