Hybrid Diagnostic Approaches Integrating Fuzzy Logic and Neural Networks for Parkinson’s Disease

Parkinson’s Disease (PD) is a movement-related and non-motor symptom neurological condition that requires early diagnosis and treatment. Fuzzy Logic and Neural Network Diagnostic hybrids are more accurate and reliable. The diagnostic approaches of PD are not sensitive to early PD, are subjective in assessing symptoms, and lack standardization. Such problems restrict treatment choices, thereby preventing a favorable patient outcome. In the PD Hybrid Diagnostic Approach (PD-HDA), fuzzy logic is utilized to address uncertainties in clinical data, and neural networks are employed to identify complex patterns in multimodal data. The PD-HDA design features structured selection and data fusion, which enhance diagnostic accuracy and constrain method variability. The images of hand tremors, gait analysis, and speech patterns are categorized using a CNN to reveal their complex properties. Fuzzy Logic and CNNs enhance the classification of PD stages and patient responses to symptoms. The PD-HDA model increases accuracy, sensitivity, and specificity during testing. The hybrid methods can be useful for early identification of PD and provide individualized care, leading to improved patient outcomes.

@article{Almozani2025,
  title     = {Hybrid Diagnostic Approaches Integrating Fuzzy Logic and Neural Networks for Parkinson’s Disease},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Marwah Muwafaq Almozani and Hüseyin Demirel},
  doi       = {10.14569/IJACSA.2025.0161295},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161295}
}