Jaccard Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices and Its Application to Diagnosis of Parkinson’s Disease
Publish place: 9th Iranian Joint Congress on Fuzzy and Intelligent Systems
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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FJCFIS09_060
تاریخ نمایه سازی: 10 اردیبهشت 1401
Abstract:
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.
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Authors
Samet Memiş
Department of Computer Engineering Faculty of Engineering and Natural Sciences İstanbul Rumeli University İstanbul, Turkey
Serdar Enginoğlu
Department of Mathematics Faculty of Arts and Sciences Çanakkale Onsekiz Mart University Çanakkale, Turkey
Uğur Erkan
Department of Computer Engineering Faculty of Engineering Karamanoğlu Mehmetbey University Karaman, Turkey