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Feature Selection Using Multiple Ranks with Majority Vote-Based Relative Aggregate Scoring Model for Parkinson Dataset

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Proceedings of International Conference on Data Science and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 287))

Abstract

Parkinson’s disease is a progressive neural disorder that affects the central as well as the entire nervous system. The major symptoms of the disease are difficulty in walking, slowness in movement, and difficulty in coordinating with brain and body movements. It involves various other complications including depression and sleeping disorders. There are various causes for Parkinson's disease such as environmental factors, genetics, and more. With the technological growth, machine learning techniques have been employed in various fields including health care in many aspects. Owing to the advantage of machine learning techniques, this paper presents the model that selects the significant features among various attributes from Parkinson’s dataset. The proposed method employs multiple ranks with a majority vote-based relative aggregate scoring method for selecting key features among various features that contribute toward the disease among patients. The proposed method ranks the attributes using various methods with various weights which are computed based on the accuracy of the method, and the obtained ranks are then converted to scores based on the obtained majority votes. The results obtained from the methods for each attribute are computed using the relative aggregate score approach from which the attributes having the highest average score are selected. The proposed model provides better results in selecting the most imperative attribute from the underlying Parkinson dataset. The experimental analysis has been made with three datasets from which it is clear that the performance of the proposed model provides effective results with 91.027% of accuracy.

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Author information

Authors and Affiliations

  1. IBM India Pvt. Ltd., Pollachi, India

    Arunraj Gopalsamy

  2. Sri Krishna Arts and Science College, Coimbatore, India

    B. Radha

Authors
  1. Arunraj Gopalsamy
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  2. B. Radha
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Editor information

Editors and Affiliations

  1. Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sarbani Roy

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Chandreyee Chowdhury

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

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Gopalsamy, A., Radha, B. (2022). Feature Selection Using Multiple Ranks with Majority Vote-Based Relative Aggregate Scoring Model for Parkinson Dataset. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_1

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