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Title PREDICTION MODEL FOR THE REDUCTION OF YOUNG DRUG ABUSERS

Author(s) MAHA MUGHAL

Abstract The problem of drug addiction is increasing day by day at alarming levels. The understanding of addictive disorders and psychiatric pathologies has become easier through new computational technologies and techniques. Collection and comparison of data has become more efficient through the usage of new emerging AI trends. Technique of digital phenotyping paves the way for capturing characteristics of different psychiatric disorders in patients. Likewise, machine learning is helping the doctors in the classification of patients based on different patterns detected through data. Almost 40,000 people are becoming drug addicts in Pakistan annually. Drug addiction problem is caused due to many reasons like peer influence, curiosity or family disturbances. This research focuses on those drug addicts who have stepped in this social evil due to some family issues. The best possible solution for controlling this social evil is to bring awareness among the parents about the effects of their behaviors on the mental and physical health of the child. In order to do that predictive analysis was applied to forecast the upcoming trends and events in drug addiction due to family disturbances. First systematic literature review was conducted for deducing the major family factors effecting the health of child from extensive literature. Six family factors were inferred parent child activities, family structure, parent child communication, parents involved in drugs, parent monitoring and supervision, and strategies for family management. After the SLR, survey was conducted from drug addicts in order to gather data for predictive analysis. During the survey age of the patients was limited to 13 till 25. Total 3528 patients have been selected for the study. However, twin cities have been targeted for the data collection purpose. After the collection, data was wrangled and labeled properly and three classification models were applied Naïve Bayes, Decision Tree, and Random Forest. Decision Tree had the maximum accuracy percentage of 96%. After that upcoming trends were depicted for the six factors. The current values of family factors are 747, 430, 1018, 296, 1497, and 437 respectively. The predicted values are 4455, 2321, 3895, 5353, 25417, and 9098 respectively. By reviewing these values it’s evident that government needs to take quick actions against this social evil and parents need to be acknowledged about the impact of their actions on children.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2023-02-09

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