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Comparison of Different Decision Tree Algorithms for Predicting the Heart Disease

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Machine Learning, Image Processing, Network Security and Data Sciences (MIND 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1241))

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Abstract

Data mining procedures are utilized to extract meaningful information for effective knowledge discovery. Decision tree, a classification method, is an efficient method for prediction. Seeing its importance, this paper compares decision tree algorithms to predict heart disease. The heart disease data sets are taken from Cleveland database, Hungarian database and Switzerland database to evaluate the performance measures. 60 data records for training and 50 data records for testing were taken as input for comparison. In order to evaluate the performance, fourteen attributes are considered to generate confusion matrices. The results exhibited that the algorithm that highest accuracy rates for predicting heart disease is Random forest, and thus can be considered as the best procedure for prediction.

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Authors and Affiliations

  1. Department of Physics and Computer Science, Dayalbagh Educational Institute, Agra, India

    Deepak Saraswat & Preetvanti Singh

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  1. Deepak Saraswat
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  2. Preetvanti Singh
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Correspondence to Deepak Saraswat .

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Editors and Affiliations

  1. National Institute of Technology Silchar, Silchar, India

    Arup Bhattacharjee

  2. National Institute Of Technology Silchar, Silchar, India

    Samir Kr. Borgohain

  3. National Institute of Technology Silchar, Silchar, India

    Badal Soni

  4. National Institute of Technology Kurukshetra, Kurukshetra, India

    Gyanendra Verma

  5. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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Saraswat, D., Singh, P. (2020). Comparison of Different Decision Tree Algorithms for Predicting the Heart Disease. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_21

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  • DOI: https://doi.org/10.1007/978-981-15-6318-8_21

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