Drug Addiction Prediction System by Machine Learning Techniques : A case study
Dr.M.DEEPA
, S SRI RANJANI , SOWMIYA.V , Tamizhan.E , Venkat Vijay.M.P
Addiction Drugs and alcohol, Logistic regression , Machine learning, Prediction system
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.
"Drug Addiction Prediction System by Machine Learning Techniques : A case study", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 2, page no.94 - 98, February-2023, Available :https://ijsdr.org/papers/IJSDR2302018.pdf
Volume 8
Issue 2,
February-2023
Pages : 94 - 98
Paper Reg. ID: IJSDR_203820
Published Paper Id: IJSDR2302018
Downloads: 000347248
Research Area: Science & Technology
Country: pachal, namakkal, tamil nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
