Figure 1

Deep learning-based blood-biochemistry clocks accurately predict chronological age. (A) Prediction accuracy of the best-performing model. The model trained on 24 parameters achieved an R² of 0.57 and an MAE of 5.7 years. (B) The design of the deep learning study that used blood-biochemistry data to predict an individual’s age. Blood samples of nonsmokers were first preprocessed and normalized as previously described⁸. Next, arbitrage ranking based on 320 RF models was applied to facilitate the selection of the most appropriate feature space with maximum samples available. Afterward, missing values were reconstructed using an autoregressive model with a view towards increasing the training sets, and the resulting feature sets were used to train and test DNNs for predicting patient age and smoking status. (C) Feature importance plot. Fasting glucose, sex, and RDW exhibited higher relative importance scores than other features used in model training. 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.