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Multiclass Classification in Machine Learning Algorithms for Disease Prediction

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Advanced Informatics for Computing Research (ICAICR 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1575))

Abstract

This paper proposes multiclass classification using different symptoms of patients into 40 different classes. This paper also represents the comparative study of the performance of four different Machine Learning models on the test symptoms data of the patients and suggests the most effient model to classify into 40 classes. Random Forest, Support Vector Machine (SVM), Naive Bayes and Decision tree are used for building the model. The performance of the algorithms is being analyzed on the parameter like accuracy, precision, and F1-score. The results reveal that Random Forest and Decision Tree are more accurate than other machine learning algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India

    Pallavi Tiwari, Deepak Upadhyay, Bhaskar Pant & Noor Mohd

Authors
  1. Pallavi Tiwari
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  2. Deepak Upadhyay
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  3. Bhaskar Pant
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  4. Noor Mohd
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Corresponding author

Correspondence to Pallavi Tiwari .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Malaysia

    Kamarul Bin Ghazali Hawari

  4. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Saint Mary's University, Halifax, NS, Canada

    Pawan Lingras

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Tiwari, P., Upadhyay, D., Pant, B., Mohd, N. (2022). Multiclass Classification in Machine Learning Algorithms for Disease Prediction. In: Luhach, A.K., Jat, D.S., Hawari, K.B.G., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2021. Communications in Computer and Information Science, vol 1575. Springer, Cham. https://doi.org/10.1007/978-3-031-09469-9_9

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  • DOI: https://doi.org/10.1007/978-3-031-09469-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09468-2

  • Online ISBN: 978-3-031-09469-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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