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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Drug Addiction Prediction System by Machine Learning Techniques : A case study |
| Authors Name: | Dr.M.DEEPA , S SRI RANJANI , SOWMIYA.V , Tamizhan.E , Venkat Vijay.M.P |
| Unique Id: | IJSDR2302018 |
| Published In: | Volume 8 Issue 2, February-2023 |
| Abstract: | Today's youth in society, as well as the population of Tamil Nadu, face a serious threat from drug and alcohol addiction. Therefore, as responsible members of society, we must act to shield these impressionable minds from potentially fatal addiction. In this article, we take a machine learning-based approach to predicting the likelihood of developing a drug addiction. First, by speaking with doctors, drug addicts, and reading pertinent publications and write-ups, we identify several key causes of addiction.Next, we gather information from both addicted and non-addicted individuals. We apply nine notable machine learning algorithms—k-nearest neighbors, logistic regression, SVM, nave bayes, classification and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine—on the preprocessed data set. We then assess how well each of these classifiers performs in terms of some key performance metrics. By achieving an accuracy close to 95.01%, logistic regression is determined to surpass all other classifiers in terms of all measures. The findings of CART, on the other hand, are subpar, with an accuracy of about 50.37% after using principal component analysis. |
| Keywords: | Addiction Drugs and alcohol, Logistic regression , Machine learning, Prediction system |
| Cite Article: | "Drug Addiction Prediction System by Machine Learning Techniques : A case study", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 2, page no.94 - 98, February-2023, Available :http://www.ijsdr.org/papers/IJSDR2302018.pdf |
| Downloads: | 000337212 |
| Publication Details: | Published Paper ID: IJSDR2302018 Registration ID:203820 Published In: Volume 8 Issue 2, February-2023 DOI (Digital Object Identifier): Page No: 94 - 98 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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