This model evaluates the risk of drug consumption for an individual based on features like demographic information, personality traits, impulsivity and sensation seeking.A different model is applied for predicting risk for each drug and best one is chosen from the results. The data set primarily contains information about central nervous system psychoactive drugs like heroin, caffeine, cannabis and other drugs. Different features are selected for each drug as per their importance in designing a model which gives us better insights about the risk of drug consumption and the characteristics of person.
Input variables : Demographic information ,Personality traits, Sensation seeking, Impulsivity
Output Variables : Drug Consumption Risk
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : researchgate.net
| Model Category | : | Public |
| Date Published | : | October, 2017 |
| Healthcare Domain | : | Provider |
| Code | : | Not available |
| Behavioral Health |