Abstract:
Drug addiction is a significant public health challenge worldwide, with its impact on indi-
viduals, families, and communities increasing in fast urbanizing countries. In Bangladesh,
the rapid growth of cities, socio-economic inequities, cultural shifts, and increased drug ac-
cessibility have led to complicated addiction patterns that require quick treatment. Factors
such as peer pressure, unemployment, mental health issues, and family history of substance
use play crucial roles in the onset and progression of addiction. Machine learning technolo-
gies have emerged as strong tools in forecasting complicated human behaviors, including
addiction risks. This study uses Logistic Regression, Decision Trees, Random Forest, Sup-
port Vector Machine, Naive Bayes, AdaBoost, KNN, and MLPClassifier to find significant
factors of drug addiction in Bangladesh and construct a risk prediction model. This model
assesses the probability of addiction at individual and community levels, providing data
for politicians and healthcare practitioners to plan focused therapies. these are result of
ten models. Logistic Regression result is 0.80, Decision Tree result is 0.77, Random Forest
result is 0.79, SVM result is 0.80, Naive Bayes result is 0.80, AdaBoost result is 0.81, KNN
result is 0.77, MLPClassifier result is 0.79, Gradient Boosting result is 0.80, CatBoost re-
sult is 0.81This research fills a vital information gap by blending demographic, behavioral,
and familial data to offer actionable insights. By adopting a data-driven approach, this
study aims to provide a deeper knowledge of the distinct risk profiles of individuals and
communities in Bangladesh. The use of predictive modeling methods can offer a more
exact way to quantify addiction risks, enabling healthcare practitioners and governments
to spend resources more effectively and devise focused therapies. The validated predictive
model will evaluate the chance of drug addiction for individuals in Bangladesh, offering
a risk score ranging from 0 to 100% and dividing individuals into low-risk and high-risk
groups based on important demographic, psychological, and environmental factors. The
findings will enable the development of intervention programs and evidence-based policy
recommendations aimed at lowering addiction risks. In conclusion, this study serves as
a significant step in addressing drug addiction in Bangladesh, producing a healthier and
more supportive society.
