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An efficient Apriori algorithm for frequent pattern in human intoxication data

  • S.I. : Intelligence for Systems and Software Engineering
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Abstract

Addicts and non-addicts can be distinguished by analyzing social behaviors and activities. An attempt was made in the study to discover the main rules that cause people to get hooked. We utilized an open-source dataset with 474 total instances and 212 total addicted individuals. They asked 50 questions during the data collection process. All of the questions were created using the Index of Addiction Severity and with the assistance of drug addiction psychologists. In this study, we utilized the Apriori algorithm to extract the most important rules from the dataset. By following this guideline, it will be clear whether or not someone is hooked based on their social conduct. The Apriori algorithm was used to find rules from the dataset, and eight significant rules were discovered, with a confidence level of 95% and a support level of 45%.

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Acknowledgements

Md. Mehedi Hassan conceived the concept in its whole, and under his supervision, all authors participated to the preparation of this comprehensive work.

Funding

This research received no external funding

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Authors and Affiliations

  1. Computer Science and Engineering, North Western University, Khulna, Bangladesh

    Md. Mehedi Hassan, Sadika Zaman & M. Raihan

  2. Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh

    Swarnali Mollick

  3. Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh

    Md. Mahedi Hassan

  4. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Chetna Kaushal

  5. Department of Computer Science and Engineering, Jain (Deemed-to-be University), Bengaluru, India

    Rajat Bhardwaj

Authors
  1. Md. Mehedi Hassan
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  2. Sadika Zaman
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  3. Swarnali Mollick
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  4. Md. Mahedi Hassan
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  5. M. Raihan
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  6. Chetna Kaushal
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  7. Rajat Bhardwaj
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Contributions

MMH made significant contributions to the study’s conceptualization and design. SZ and MR involved in evaluating data and preparing this report properly. Other authors assisted with the production of the manuscript’s draft version. All authors examined the results and gave final approval to the manuscript’s final version.

Corresponding author

Correspondence to Chetna Kaushal.

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Hassan, M.M., Zaman, S., Mollick, S. et al. An efficient Apriori algorithm for frequent pattern in human intoxication data. Innovations Syst Softw Eng 19, 61–69 (2023). https://doi.org/10.1007/s11334-022-00523-w

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  • DOI: https://doi.org/10.1007/s11334-022-00523-w

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