Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis

Early disease diagnosis is critical in improving patient outcomes, reducing healthcare costs, and preferably timely intervention. Unfortunately, the algorithms used in conventional diagnostic technology have difficulties dealing with uncertain and imprecise medical data, which may result in either delay or misdiagnosis. This paper describes the combined framework of fuzzy logic and machine learning algorithms to improve the accuracy and reliability of early disease diagnosis. Fuzzy logic addresses imprecision in patient symptoms and variability in clinical data, while machine learning algorithms provide data analytical and predictive capabilities. The proposed system enhances the abilities and complements rule-based reasoning with a predictive model to handle imprecise inputs and deliver accurate disease risk estimation. An experimental analysis of the medical datasets of heart disease, diabetes, and cancer reveals that the proposed method enhances the accuracy, precision, and ultimately robustness of a conventional diagnostic system.

@article{Arya2024,
  title     = {Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Leena Arya and Narasimha Swamy Lavudiya and G Sateesh and Harish Padmanaban and B. V. Srinivasulu and Ravi Rastogi},
  doi       = {10.14569/IJACSA.2024.0151111},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151111}
}