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A Fuzzy Rule-Based Diagnosis of Parkinson’s Disease

  • D. Karunanithi
  • Paul Rodrigues
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Neurodegenerative brain disorder is the root cause of Parkinson’s Disease (PD). Neurodegenerative is the process of impairment of brain cells. PD is diagnosed through clinical methods. Hope this research work helps to identify the intensity of the PD. Fuzzy Inference System is used to identify the PD and its intensity. Fuzzy rules, Mamdani Fuzzy Inference, Membership Functions, and Defuzzification are the process used to obtain accurate results. Oxford Parkinson’s Disease Detection Dataset is used for this research work. Among 23 fields in the dataset, only four fields FoH, DFA, Spread1, and Spread2 are chosen for analyzing the PD diagnosis and intensity. These four fields values are categorized into three sets: one is PD affected subjects, the second set is common values for both PD affected subjects and healthy subjects, and the third set is completely healthy subjects. Intensity values are measured from low to maximum as 0–100. Eighty-one rules are framed to calculate the PD intensity. We hope this FIS model is a novel method for identifying the PD intensity and helps doctors to diagnose and treat the patients in an effective way.

Keywords

Machine Learning Fuzzy Fuzzy Inference System Parkinson’s Disease 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringManonmaniam Sundaranar UniversityTirunelveliIndia
  2. 2.Computer Science and EngineeringKing Khalid UniversityAbhaSaudi Arabia

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