Jaccard Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices and Its Application to Diagnosis of Parkinson’s Disease

This paper introduces a new similarity measure of fuzzy parameterized fuzzy soft matrices (fpfs-matrices), i.e., Jaccard pseudo-similarity of fpfs-matrices. We then provide its basic properties. Afterwards, we apply it to the diagnosis of Parkinson’s Disease (PD), improving a machine learning (ML) approach. Next, we compare our approach with the well-known ML approaches, such as Naïve Bayes, 𝒌-Nearest Neighbor (𝒌NN), Support Vector Machine (SVM), Fuzzy 𝒌NN, Decision Trees (DT), Boosted Trees (BT), Adaptive Boosting Tree (AdaBoost), and Random Forest (RF) in terms of accuracy, specificity, and sensitivity. The results manifest that the proposed approach makes a more accuratediagnosis of PD than the others.

Keywords:

soft sets , fpfs-matrices , similarity measure , Parkinson’s disease , machine learning