A Case Study of a Drug Addiction Prediction System Using Machine Learning Techniques
Keywords:
Addiction Drugs and Alcohol, Logistic Regression, Machine Learning, Prediction SystemAbstract
Today's youth, as well as the entire Tamil Nadu population, are at risk of drug and alcohol addiction. We must take action as responsible citizens to shield these developing minds from potentially lethal addiction. In this piece, we'll using machine learning to forecast the chance of becoming addicted to drugs. First, through talking to medical professionals, drug users, and reading pertinent articles and publications, we pinpoint a number of primary causes of addiction. After that, we gather data from individuals who are hooked as well as those who are not. We implement nine prominent machine learning algorithms on the preprocessed data set: random forest, multilayer perception, logistic regression, SVM, nave bayes, k-nearest neighbors, and gradient boosting machine. Next, we assess the performance of each of these classifiers using important performance measures. With an accuracy close to 95.01 percent, logistic regression is determined to outperform all other classifiers on all measures. CART's findings, on the other hand, are subpar, with an accuracy of approximately 50.37 percent after using principal component analysis.
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