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Document details - Machine Learning Approaches to build Predictive System for Parkinsons Disease: A Comparative Study and Speculation

Journal Volume 11, Issue 4, July - August 2022, Article 10052281 Sumit Das, Utsab Ray, Sabyasachi Sen, Dipansu Mondal, Karabi Ganguly, Ira Nath , " Machine Learning Approaches to build Predictive System for Parkinsons Disease: A Comparative Study and Speculation" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 11, Issue 4, July - August 2022 , pp. 039-047 , ISSN 2278 - 6856.

Machine Learning Approaches to build Predictive System for Parkinsons Disease: A Comparative Study and Speculation

    Sumit Das, Utsab Ray, Sabyasachi Sen, Dipansu Mondal, Karabi Ganguly, Ira Nath

Abstract

Abstract: Orchestration of Machine Learning techniques has turned up to be consequential and notable to manifest the approaches in implementing the technologies to diminish several discrepancies and barriers that are prevailing in various sectors especially healthcare or biomedical field. In this paper, we have worked characteristically with datasets of Parkinson’s Disease to predict the disease which predominantly undergone miss predictions and treatments previously, as well as machine learning modeling by developing a predictive system based on a relative comparison between all other algorithms of machine learning binary classification algorithms, have been done to develop a pathway for proper examination of this disease to shrink several distinct deviations or dissimilitude that lead to discrepancies and additional risks. Thus, generating effective decision assistance for Parkinson’s disease prediction in this research. This work moreover contributes to narrowing the research gap in the development of effective decision support systems for medical applications by drawing analytical case results from several algorithms. Keywords: Machine Learning, Healthcare, Parkinson’s Disease, Predictive System, Classification Algorithms.

  • ISSN: 22786856
  • Source Type: Journal
  • Original language: English

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