Machine learning and network-based classification approach in opium and tobacco users: results from Fasa PERSIAN cohort study

Machine learning and network-based classification approach in opium and tobacco users: results from Fasa PERSIAN cohort study. Download PDF Abstract Opium and tobacco are widely used in parts of the Middle East and South Asia, yet their individual and combined effects on routine hematologic and biochemical markers are not fully understood. We examined how tobacco smoking (TS), opium use (OU), and their combination (OUTS) relate to laboratory parameters and the ability to distinguish user groups from never-users (Normal).

We analyzed 3497 participants from the baseline Fasa PERSIAN Cohort, classified into Normal, TS, OU, and OUTS groups. Group-specific correlation networks and community detection were used to explore network structure in clinical laboratory parameters. Multiple machine learning models were trained for classification using standardized features and evaluated by stratified 5-fold cross-validation.

An exploratory OWA ensemble was additionally evaluated using validation-based selection and an independent test set. Performance was assessed using accuracy, sensitivity, specificity, ROC AUC, and balanced accuracy. Network topology differed across all four groups, with the greatest disruption in OUTS, particularly in lipid–liver function correlations and central nodes such as HDL.C.

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