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Prediction of alcohol abused individuals using artificial neural network

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Abstract

In this work an artificial neural network (ANN) based approach is proposed for prediction of alcohol user. Two ANN modules are designed, ANN-D to predict a person is an alcohol user or not and ANN-C to predict when it is used. The features used are age, gender, country, ethnicity, education, neuroticism, openness to experience, extraversion, agreeableness, conscientiousness, impulsive, sensation seeing, etc. Input features are given to the ANN-D module to predict alcohol user. ANN-C module predicts the use of alcohol in terms of time such as day, week, month, year, decade, over decades, etc. The accuracy of the ANN-D module is found to be 98.7% and ANN-C module is 49.1%. Hence the proposed method can be used efficiently for predicting alcohol user.

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Authors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Divya Kumari, Sumran Kilam, Priyanka Nath & Aleena Swetapadma

Authors
  1. Divya Kumari
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  2. Sumran Kilam
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  3. Priyanka Nath
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  4. Aleena Swetapadma
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Corresponding author

Correspondence to Aleena Swetapadma.

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Kumari, D., Kilam, S., Nath, P. et al. Prediction of alcohol abused individuals using artificial neural network. Int. j. inf. tecnol. 10, 233–237 (2018). https://doi.org/10.1007/s41870-018-0094-3

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  • DOI: https://doi.org/10.1007/s41870-018-0094-3

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