Analysis of Classification Algorithms for Predicting Parkinson’s Disease and Applications in the Field of Cybersecurity

U. Sumalatha, K. Krishna Prakasha, Srikanth Prabhu, Vinod C. Nayak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Parkinson’s disease, which affects millions of people worldwide, is a term used to describe a neurological and neurodegenerative movement disorder. Common symptoms include a loss of automatic motions and muscle rigidity, which ultimately result in problems with balance, coordination, and walking. The patient’s physical, emotional, and mental health gradually worsens as a result of these symptoms. Before the patient’s health worsens, therapeutic care can be given to lower the disease’s prognosis. It is possible to predict whether or not a person has Parkinson’s disease using machine learning classification algorithms. This can lengthen the lives of older individuals and improve their quality of life when they have Parkinson’s. This study suggests a potential technique to identify Parkinson’s disease symptoms in their early stages. Based on the speech input parameters, algorithms like Gradient Boosting, XGBoost, Random Forest, and Extra Trees Classification are used to estimate whether the individual is normal or affected by Parkinson’s disease. According to this study, the ensemble method Gradient Boosting classification algorithm outperformed other classification algorithms in terms of test accuracy rate (95%). The effectiveness of the approaches was evaluated using a reliable dataset from the UCI Machine Learning library.

Original languageEnglish
Title of host publicationApplications and Techniques in Information Security - 13th International Conference, ATIS 2022, Revised Selected Papers
EditorsSrikanth Prabhu, Shiva Raj Pokhrel, Gang Li
PublisherSpringer Science and Business Media Deutschland GmbH
Pages155-163
Number of pages9
ISBN (Print)9789819922635
DOIs
Publication statusPublished - 2023
Event13th International Conference on Applications and Techniques in Information Security, ATIS 2022 - Manipal, India
Duration: 30-12-202231-12-2022

Publication series

NameCommunications in Computer and Information Science
Volume1804 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference13th International Conference on Applications and Techniques in Information Security, ATIS 2022
Country/TerritoryIndia
CityManipal
Period30-12-2231-12-22

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics

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