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Research Article | Open Access

Establishment of a diagnostic model of coronary heart disease in elderly patients with diabetes mellitus based on machine learning algorithms

Hu XU1,2,3,*, Wen-Zhe CAO1,4,*, Yong-Yi BAI2, Jing DONG5, He-Bin CHE5, Po BAI6, Jian-Dong WANG1,3(), Feng CAO1,2(), Li FAN1,2()
Chinese PLA Medical School, Chinese PLA General Hospital, Beijing, China
Department of Cardiology, the Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
Department of General Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing, China
Institute of Geriatrics, the Second Medical Center, Chinese PLA General Hospital, Beijing, China
Medical Big Data Research Center & National Engineering Laboratory for Medical Big Data Application Technology, Chinese PLA General Hospital, Beijing, China
Department of Respiratory Diseases, Chinese PLA Rocket Force Characteristic Medical Center, Beijing, China

*The authors contributed equally to this manuscript

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Abstract

OBJECTIVE

To establish a prediction model of coronary heart disease (CHD) in elderly patients with diabetes mellitus (DM) based on machine learning (ML) algorithms.

METHODS

Based on the Medical Big Data Research Centre of Chinese PLA General Hospital in Beijing, China, we identified a cohort of elderly inpatients (≥ 60 years), including 10,533 patients with DM complicated with CHD and 12,634 patients with DM without CHD, from January 2008 to December 2017. We collected demographic characteristics and clinical data. After selecting the important features, we established five ML models, including extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), adaptive boosting (Adaboost) and logistic regression (LR). We compared the receiver operating characteristic curves, area under the curve (AUC) and other relevant parameters of different models and determined the optimal classification model. The model was then applied to 7447 elderly patients with DM admitted from January 2018 to December 2019 to further validate the performance of the model.

RESULTS

Fifteen features were selected and included in the ML model. The classification precision in the test set of the XGBoost, RF, DT, Adaboost and LR models was 0.778, 0.789, 0.753, 0.750 and 0.689, respectively; and the AUCs of the subjects were 0.851, 0.845, 0.823, 0.833 and 0.731, respectively. Applying the XGBoost model with optimal performance to a newly recruited dataset for validation, the diagnostic sensitivity, specificity, precision, and AUC were 0.792, 0.808, 0.748 and 0.880, respectively.

CONCLUSIONS

The XGBoost model established in the present study had certain predictive value for elderly patients with DM complicated with CHD.

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Journal of Geriatric Cardiology
Volume 19 Issue 6,
June 2022
Pages 445-455
DOI: 10.11909/j.issn.1671-5411.2022.06.006
Cite this article:
XU H, CAO W-Z, BAI Y-Y, et al. Establishment of a diagnostic model of coronary heart disease in elderly patients with diabetes mellitus based on machine learning algorithms. Journal of Geriatric Cardiology, 2022, 19(6): 445-455. https://doi.org/10.11909/j.issn.1671-5411.2022.06.006
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Clinical characteristics of all patients.

CharacteristicsResearch group (n = 10,533)Control group (n = 12,634)P-value
Data are presented as n (%). *Presented as median (interquartile range).
Demographics
　Male 5537 (52.6%) 6168 (48.8%) < 0.001
　Age, yrs 69 (64–74)* 68 (63–73)* < 0.001
　Body mass index, kg/m2 25.7 (23.6–28.0)* 25.3 (23.1–27.6)* < 0.001
　Systolic blood pressure, mmHg 138 (126–150)* 138 (126–150)* 0.606
　Diastolic blood pressure, mmHg 75 (68–82)* 80 (70–85)* < 0.001
Blood electrolyte
　Ca2+, mmol/L 2.25 (2.16–2.32)* 2.22 (2.11–2.31)* < 0.001
　P5+, mmol/L 1.09 (0.93–1.24)* 1.10 (0.87–1.25)* 0.002
　Mg2+, mmol/L 0.86 (0.80–0.92)* 0.85 (0.76–0.91)* < 0.001
　K+, mmol/L 3.91 (3.63–4.17)* 3.90 (3.54–4.18)* < 0.001
　Na+, mmol/L 141.5 (139.1–143.5)* 141.7 (138.6–143.7)* 0.032
　Cl−, mmol/L 103.8 (101.2–106.1)* 103.2 (99.8–105.7)* < 0.001
　CO2, mmol/L 25.9 (23.6–27.9)* 25.7 (22.9–27.8)* < 0.001
Glycemic control
　Hemoglobin A1c, % 6.30 (3.77–7.60)* 3.78 (3.75–6.80)* < 0.001
　Glycated serum protein, μmol/L 171.0 (120.1–230.3)* 136.2 (120.1–218.1)* < 0.001
Myocardial enzyme
　Troponin T, ng/mL 0.006 (0.003–0.016)* 0.003 (0.003–0.003)* < 0.001
　Creatine kinase, U/L 66.3 (45.2–101.3)* 57.8 (31.6–89.7)* < 0.001
　Creatine kinase-MB, U/L 7.2 (0.6–13.9)* 9.9 (0.5–14.9)* < 0.001
　Lactate dehydrogenase, U/L 156.4 (135.4–183.7)* 150.0 (123.4–175.1)* < 0.001
　Pro-B-type natriuretic peptide, pg/mL 59.5 (5.0–315.9)* 5.0 (5.0–5.1)* < 0.001
Blood coagulation
　Thrombin time, s 16.2 (15.4–17.0)* 16.0 (12.7–16.9)* < 0.001
　Activated partial thromboplastin time, s 22.5 (22.3–35.7)* 22.4 (22.2–34.5)* < 0.001
　Prothrombin time, s 13.3 (12.8–14.1)* 13.3 (12.7–14.3)* 0.988
　Prothrombin activity, % 96 (86–105)* 96 (82–106)* < 0.001
　International normalized ratio 1.03 (0.97–1.07)* 1.03 (0.97–1.07)* 0.041
　Fibrinogen, g/L 3.33 (2.81–3.98)* 3.19 (2.53–3.88)* < 0.001
　D-dimer, μg/mL 0.30 (0.01–0.52)* 0.01 (0.01–0.37)* < 0.001
Routine blood + C-reactive protein
　Hemoglobin, g/dL 132 (120–143)* 131 (116–142)* < 0.001
　Red blood cell count, × 1012/L 4.35 (3.97–4.70)* 4.30 (3.84–4.67)* < 0.001
　White blood cell count, × 109/L 6.42 (5.27–7.73)* 6.03 (4.77–7.46)* < 0.001
　Neutrophil, % 0.61 (0.54–0.68)* 0.58 (0.50–0.66)* < 0.001
　Lymphocyte, % 0.28 (0.21–0.35)* 0.29 (0.19–0.36)* 0.225
　Monocyte, % 0.06 (0.05–0.07)* 0.06 (0.04–0.07)* < 0.001
　Eosinophil, % 0.02 (0.01–0.03)* 0.02 (0.01–0.03)* < 0.001
　Basophil, % 0.003 (0.002–0.005)* 0.003 (0.002–0.005)* < 0.001
　Hematocrit, % 0.40 (0.37–0.43)* 0.40 (0.37–0.44)* < 0.001
　Mean corpuscular volume, fl 90.0 (87.1–92.9)* 89.4 (85.8–92.4)* < 0.001
　Mean corpuscular hemoglobin, pg 30.4 (29.3–31.4)* 30.2 (28.9–31.3)* < 0.001
　Mean corpuscular hemoglobin concentration, g/L 337 (329–344)* 336 (326–344)* < 0.001
　Red cell distribution width, % 12.8 (12.3–13.4)* 12.8 (12.2–13.3)* < 0.001
　Platelet count, × 109/L 198 (162–240)* 193 (150–237)* < 0.001
　Mean platelet volume, fl 10.6 (9.9–11.2)* 10.4 (9.7–11.2)* < 0.001
　C-reactive protein, mg/dL 0.1 (0–0.348)* 0.0 (0–0.328)* < 0.001
Routine urine
　Specific gravity 1.014 (1.010–1.019)* 1.013 (1.005–1.019)* < 0.001
　pH 5.5 (5.0–6.5)* 5.5 (5.0–6.5)* < 0.001
Blood biochemical
　Alanine aminotransferase, U/L 17.2 (12.3–25.6)* 15.1 (10.1–22.0)* < 0.001
　Aspartate aminotransferase, U/L 16.6 (13.4–21.8)* 15.3 (11.9–19.7)* < 0.001
　Total protein, g/L 67.7 (63.5–71.9)* 65.7 (60.4–70.2)* < 0.001
　Albumin, g/L 40.5 (37.7–42.9)* 39.7 (35.3–42.4)* < 0.001
　Total bilirubin, μmol/L 10.1 (7.4–13.5)* 9.4 (6.4–13.0)* < 0.001
　Direct bilirubin, μmol/L 3.1 (2.2–4.3)* 2.8 (1.8–4.1)* < 0.001
　Total bile acid, μmol/L 1.4 (0.002–3.7)* 2.4 (0.003–4.6)* < 0.001
　Homocysteine, μmol/L 0.82 (0.76–13.20)* 1.10 (0.77–13.10)* < 0.001
　Alkaline phosphatase, U/L 65.6 (52.9–79.9)* 64.7 (48.9–80.7)* < 0.001
　Glutamyl transpeptidase, U/L 22.6 (15.8–34.6)* 19.9 (12.5–32.1)* < 0.001
　Glucose, mmol/L 7.02 (5.57–9.46)* 6.37 (4.97–8.32)* < 0.001
　Blood urea nitrogen, mmol/L 5.56 (4.46–6.91)* 5.29 (4.04–6.59)* < 0.001
　Creatinine, μmol/L 70.7 (58.5–84.5)* 64.1 (51.4–78.0)* < 0.001
　Uric acid, μmol/L 296.3 (239.2–359.0)* 269.0 (196.1–333.7)* < 0.001
　Total cholesterol, mmol/L 3.80 (2.99–4.65)* 3.92 (1.24–4.85)* 0.290
　Triglyceride, mmol/L 1.27 (0.85–1.81)* 1.11 (0.23–1.69)* < 0.001
　Apolipoprotein A1, g/L 0.95 (0.23–1.23)* 0.77 (0.23–1.22)* < 0.001
　Apolipoprotein B, g/L 0.61 (0.19–0.88)* 0.50 (0.18–0.91)* < 0.001
　High-density lipoprotein cholesterol, mmol/L 0.99 (0.79–1.19)* 0.94 (0.08–1.20)* < 0.001
　Low-density lipoprotein cholesterol, mmol/L 2.18 (1.54–2.90)* 2.26 (0.09–3.04)* < 0.001
　Lipoprotein(a), mg/dL 0.00 (0–16.82)* 0.00 (0–15.51)* 0.543
　Apolipoprotein A1/Apolipoprotein B 1.28 (1.19–1.51)* 1.28 (1.18–1.39)* < 0.001
　Estimated glomerular filtration rate, mL/min per 1.73 m2 98.2 (80.3–117.9)* 108.1 (88.2–135.5)* < 0.001

Performance of the five models on the testing sets.

Test setAUC95% CIPrecisionAccuracyF1-scoreSensitivitySpecificity
Adaboost: adaptive boosting; AUC: area under the curve; DT: decision tree; LR: logistic regression; RF: random forest; XGBoost: extreme gradient boosting.
XGBoost 0.851 0.841–0.861 0.778 0.773 0.732 0.690 0.841
RF 0.845 0.834–0.855 0.789 0.778 0.735 0.688 0.851
DT 0.823 0.812–0.834 0.753 0.757 0.714 0.679 0.820
Adaboost 0.833 0.822–0.844 0.750 0.757 0.715 0.683 0.816
LR 0.731 0.718–0.744 0.689 0.686 0.607 0.542 0.803

Clinical characteristics of newly recruited subjects.

CharacteristicsResearch group (n = 3116)Control group (n = 4331)P-value
Data are presented as n (%). *Presented as median (interquartile range).
Demographics
　Male 1816 (58.3%) 2321 (53.6%) < 0.001
　Age, yrs 68 (64–74)* 67 (64–72)* < 0.001
　Body mass index, kg/m2 25.6 (23.5–27.7)* 25.3 (23.2–27.4)* < 0.001
　Systolic blood pressure, mmHg 138 (124–150)* 138 (126–150)* 0.082
　Diastolic blood pressure, mmHg 75 (67–82)* 78 (70–85)* < 0.001
Blood electrolyte
　Ca2+, mmol/L 2.25 (2.17–2.33)* 2.22 (2.11–2.3)* < 0.001
　P5+, mmol/L 1.08 (0.93–1.21)* 1.10 (0.90–1.24)* 0.209
　Mg2+, mmol/L 0.85 (0.79–0.91)* 0.83 (0.75–0.89)* < 0.001
　K+, mmol/L 3.84 (3.58–4.11)* 3.88 (3.48–4.19)* 0.326
　Na+, mmol/L 141.1 (139.1–142.7)* 141.1 (138.4–143.0)* 0.238
　Cl−, mmol/L 102.8 (100.4–104.8)* 102.0 (98.7–104.5)* < 0.001
　CO2, mmol/L 26.6 (24.6–28.6)* 26 (23.3–27.9)* < 0.001
Glycemic control
　Hemoglobin A1c, % 6.8 (3.8–7.9)* 3.8 (3.8–6.5)* < 0.001
　Glycated serum protein, μmol/L 180.4 (129.9–221.2)* 172.8 (139.9–201.5)* < 0.001
Myocardial enzyme
　Troponin T, ng/mL 0.009 (0.004–0.019)* 0.003 (0.003–0.008)* < 0.001
　Creatine kinase, U/L 67.1 (45.9–99.6)* 61.1 (35.5–93.8)* < 0.001
　Creatine kinase-MB, U/L 6.0 (0.5–11.7)* 10.2 (0.5–15.8)* < 0.001
　Lactate dehydrogenase, U/L 159.7 (137.7–187.0)* 152.9 (126.4–177.9)* < 0.001
　Pro-B-type natriuretic peptide, pg/mL 103.9 (26.0–355.7)* 5.0 (5.0–76.0)* < 0.001
Blood coagulation
　Thrombin time, s 15.8 (15.1–16.5)* 15.7 (14.7–16.5)* < 0.001
　Activated partial thromboplastin time, s 22.28 (22.16–22.40)* 22.22 (22.06–22.34)* < 0.001
　Prothrombin time, s 13.3 (12.8–13.9)* 13.3 (12.7–14.4)* 0.048
　Prothrombin activity, % 97 (88–105)* 96 (82–107)* 0.006
　International normalized ratio 1.02 (0.98–1.07)* 1.03 (0.97–1.07)* 0.854
　Fibrinogen, g/L 3.26 (2.79–3.85)* 3.00 (2.34–3.61)* < 0.001
　D-dimer, μg/mL 0.33 (0.23–0.55)* 0.26 (0.01–0.47)* < 0.001
Routine blood + C-reactive protein
　Hemoglobin, g/dL 132 (121–143)* 129 (114–141)* < 0.001
　Red blood cell count, × 1012/L 4.34 (3.97–4.68)* 4.25 (3.75–4.61)* < 0.001
　White blood cell count, × 109/L 6.40 (5.18–7.65)* 5.92 (4.61–7.37)* < 0.001
　Neutrophil, % 0.61 (0.54–0.67)* 0.58 (0.49–0.66)* < 0.001
　Lymphocyte, % 0.28 (0.21–0.34)* 0.28 (0.17–0.35)* 0.008
　Monocyte, % 0.06 (0.05–0.08)* 0.06 (0.05–0.07)* < 0.001
　Eosinophil, % 0.02 (0.01–0.03)* 0.02 (0.01–0.03)* < 0.001
　Basophil, % 0.004 (0.003–0.006)* 0.004 (0.002–0.006)* < 0.001
　Hematocrit, % 0.39 (0.36–0.42)* 0.40 (0.36–0.43)* < 0.001
　Mean corpuscular volume, fl 88.0 (85.4–90.9)* 87.7 (84.1–90.8)* < 0.001
　Mean corpuscular hemoglobin, pg 30.4 (29.3–31.5)* 30.3 (28.8–31.4)* < 0.001
　Mean corpuscular hemoglobin concentration, g/L 344 (336–351)* 342 (332–351)* < 0.001
　Red cell distribution width, % 12.6 (12.1–13.1)* 12.4 (11.9–13.0)* < 0.001
　Platelet count, × 109/L 202 (164–245)* 197 (149–239)* < 0.001
　Mean platelet volume, fl 10.5 (9.9–11.2)* 10.3 (9.5–11.1)* < 0.001
　C-reactive protein, mg/dL 0.097 (0.050–0.183)* 0.05 (0–0.117)* < 0.001
Routine urine
　Specific gravity 1.014 (1.010–1.019)* 1.012 (1.000–1.019)* < 0.001
　pH 6.0 (5.5–6.5)* 5.5 (5.0–6.0)* < 0.001
Blood biochemical
　Alanine aminotransferase, U/L 16.3 (11.7–23.7)* 14.3 (9.5–20.7)* < 0.001
　Aspartate aminotransferase, U/L 15.9 (13.0–20.6)* 14.8 (11.3–18.9)* < 0.001
　Total protein, g/L 67.8 (64.0–71.8)* 65.4 (60.1–69.5)* < 0.001
　Albumin, g/L 40.7 (38.0–43.1)* 40.1 (35.4–42.8)* < 0.001
　Total bilirubin, μmol/L 10.4 (7.5–13.9)* 9.8 (6.6–13.5)* < 0.001
　Direct bilirubin, μmol/L 3.0 (2.0–4.2)* 2.8 (1.7–4.0)* < 0.001
　Total bile acid, μmol/L 1.2 (0.001–1.7)* 1.8 (0.002–4.1)* < 0.001
　Homocysteine, μmol/L 0.79 (0.75–11.70)* 0.83 (0.76–12.40)* < 0.001
　Alkaline phosphatase, U/L 62.9 (50.6–76.8)* 61.0 (46.3–76.1)* < 0.001
　Glutamyl transpeptidase, U/L 22.0 (15.3–32.5)* 18.9 (11.8–29.6)* < 0.001
　Glucose, mmol/L 7.07 (5.66–9.57)* 6.37 (4.85–8.09)* < 0.001
　Blood urea nitrogen, mmol/L 5.44 (4.45–6.79)* 5.07 (3.78–6.37)* < 0.001
　Creatinine, μmol/L 72.6 (60.3–86.4)* 65.7 (51.8–79.6)* < 0.001
　Uric acid, μmol/L 303.3 (245.4–367.7)* 275.6 (203.1–341.6)* < 0.001
　Total cholesterol, mmol/L 3.45 (2.69–4.25)* 3.28 (1.22–4.34)* < 0.001
　Triglyceride, mmol/L 1.25 (0.85–1.77)* 0.95 (0.22–1.56)* < 0.001
　Apolipoprotein A1, g/L 0.89 (0.23–1.21)* 0.24 (0.23–1.22)* 0.066
　Apolipoprotein B, g/L 0.45 (0.19–0.79)* 0.21 (0.18–0.84)* 0.120
　High-density lipoprotein cholesterol, mmol/L 0.98 (0.77–1.18)* 0.87 (0.07–1.15)* < 0.001
　Low-density lipoprotein cholesterol, mmol/L 2.02 (1.38–2.76)* 1.83 (0.08–2.83)* < 0.001
　Lipoprotein(a), mg/dL 0.00 (0–0.65)* 0.00 (0–8.92)* < 0.001
　Apolipoprotein A1/Apolipoprotein B 1.28 (1.19–1.57)* 1.28 (1.18–1.43)* < 0.001
　Estimated glomerular filtration rate, mL/min per 1.73 m2 95.9 (78.9–115.1)* 105.4 (87.1–134.3)* < 0.001

Performance of the five models on the validation sets.

ModelAUC95% CIPrecisionAccuracyF1-scoreSensitivitySpecificity
Adaboost: adaptive boosting; AUC: area under the curve; DT: decision tree; LR: logistic regression; RF: random forest; XGBoost: extreme gradient boosting.
XGBoost 0.880 0.872–0.887 0.748 0.801 0.769 0.792 0.808
RF 0.877 0.869–0.884 0.771 0.811 0.776 0.781 0.833
DT 0.858 0.849–0.865 0.770 0.802 0.760 0.750 0.839
Adaboost 0.869 0.862–0.877 0.692 0.772 0.751 0.820 0.737
LR 0.797 0.788–0.807 0.682 0.741 0.698 0.715 0.761