Figure 2

Deep learning-based hematological clocks demonstrated accelerated aging rates in smokers and revealed patient smoking status. (A) The prediction accuracy of the best-performing model trained on feature space extended with smoking status. The model, trained on 24 parameters, achieved an R² of 0.60 and an MAE of 5.42 years (B) The log₂ aging ratio of smokers to nonsmokers by age and sex groups for the best-performing model. Smokers demonstrated a higher aging rate regardless of sex. However, these differences plateaued after 55 years of age. A log₂ aging ratio of 1 means the sample was predicted to be twice as old as a chronological age, and a log₂ aging ratio of −1 means the sample was predicted to be half as old as a chronological age. (C) The most important features in the classification of smoking status selected by the PFI method. HDL cholesterol, sex, and hemoglobin exhibited higher relative importance scores than other features used in model training. (D) The model trained on 23 parameters achieved an F1 score of 0.67 and an accuracy of 0.84. Note High-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol. RDW for red blood cell distribution width, RBC for red blood cell counts, MCV for mean corpuscular volume, ALT for alanine transaminase, MCHC for mean corpuscular hemoglobin.