A New Evolutionary Fuzzy K-Nearest Neighbor with Application to Parkinson’s Diagnosis

Key Points

• Enhanced classification accuracy was achieved using an evolutionary fuzzy k-nearest neighbor algorithm, indicating better Parkinson's diagnosis.
• Key evidence shows a substantial improvement in diagnostic metrics, surpassing traditional methods based on similar datasets.
• The observational analysis applies evolutionary algorithms to refine the k-nearest neighbor technique for disease classification.
• These findings highlight the potential for improved diagnostic tools, crucial for early detection and patient management.