Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Proposed Multi-Stage Machine Learning Algorithm-Based Scheme
3. Results
4. Discussion
5. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Abbreviation | Variable (Unit) | Description/Reference Range (RR) |
---|---|---|
SEX | Gender (sex) | (1) Male; (2) Female |
Age | Age (y/o) | Number; Years old (y/o) |
MS | Marital status | (1) Single; (2) Married, remarried, cohabiting; (3) Divorced; (4) Widowed |
EL | Education level | (1) No formal education; (2) Elementary school; (3) Secondary school; (4) High school; (5) College; (6) University; (7) Graduate school |
FI | Family income (NTD) | (1) Unwaged; (2) ≤200,000; (3) 200,001–400,000; (4) 400,001–800,000; (5) 800,001–1,200,000; (6) 1,200,001–1,600,000; (7) 1,600,001–2,000,000; (8) >2,000,000 |
BMI | Body mass index (kg/m2) | Number; Body weight/Body height2 |
BF | Body fat (%) | Number; Data collection from ©OMRON: HBF–702t |
WC | Waist circumference (cm) | Number; WC measured with a tape measure by SOP. |
HC | Hip circumference (cm) | Number; HC measured with a tape measure by SOP. |
WHR | Waist-to-hip ratio (%) | Number; Waist circumference/Hip Circumference |
Hb | Hemoglobin (g/dl) | Number; RR: Male: 13.5 < Hb < 17.5; Female: 12.0 < Hb < 16.0 |
FPG | Fasting plasma glucose (mg/dL) | Number; RR: 70 < FPG < 100 |
TG | Triglycerides (mg/dL) | Number; RR: TG ≤ 150 |
T-Cho | Total cholesterol (mg/dL) | Number; RR: 130 < T-Cho < 200 |
FT4 | Free thyroxine 4 (ng/dL) | Number; RR: 0.70 < FT4 < 1.48 |
TSH | Thyroid-stimulating hormone (μIU/mL) | Number; RR: 0.47 < TSH < 5.00 |
CRP | C-reactive protein (mg/dL) | Number, RR: CRP < 0.5 |
UP | Urine protein | Qualitative test; (1) none (2) trace (+/−) (3) + (4) ++ (5) +++ (6) ++++ |
CS | Current smoker | (1) Never; (2) Passive smoking; (3) Quit; (4) Occasional; (5) Addicted |
AD | Alcohol drinker | (1) Never; (2) Quit; (3) 1–2 times a week; (4) 3–4 times a week; (5) 5–6 times a week; (6) Addicted |
CBN | Chews betel nut (Areca catechu) | (1) Never; (2) Quit; (3) 1–3 times a week; (4) 4–5 times a week; (5) Addicted |
MB | Mealtime behavior | (0) Irregular; (1) Regular |
ET | Excise time (hours) | Time spent exercising in the past two weeks. (1) <0.5; (2) 0.5–1; (3) 1–2; (4) >2 |
ST | Sleep time (hours) | Average slee** time at night. (1) <4; (2) 4–6; (3) 6–7; (4) 7–8; (5) 8–9; (6) >9 |
HDL-C | High-density lipoprotein cholesterol (mg/dL) | Number; RR: Male: HDL-C > 40; Female: HDL-C > 50. IRR–HDL and/or ORR–HDL: the different RR values for males and females were considered. |
LDL-C | Low-density lipoprotein cholesterol (mg/dL) | Number, RR: LDL-C < 130 |
HTN | Hypertension in early stage # SBP: Systolic blood pressure (mmHg) DBP: Diastolic blood pressure (mmHg) | (0) Normal subjects: SBP < 120 and DBP < 80 (1) HTN subjects: SBP ≥ 120 and DBP ≥ 80 |
Ordinal Variable (Unit) | N (%) | Ordinal Variable (Unit) | N (%) | ||
---|---|---|---|---|---|
Gender | Male | 15,628 (51.65%) | Chews betel nut (Areca catechu) | Never | 28,784 (95.14%) |
Female | 14,627 (48.35%) | Quit | 1053 (3.48%) | ||
Marital status | Single | 4906 (16.22%) | 1–3 times a week | 264 (0.87%) | |
Married, remarried, cohabiting | 22,948 (75.85%) | 4–5 times a week | 50 (0.17%) | ||
Divorced | 1144 (3.78%) | Addicted | 104 (0.34%) | ||
Widowed | 1257 (4.15%) | Mealtime behavior | Irregular | 8384 (27.71%) | |
Education level | No formal education | 438 (1.45%) | Regular | 21,871 (72.29%) | |
Elementary school | 1958 (6.47%) | Excise time (hours) | <0.5 | 8361 (27.64%) | |
Secondary school | 1251 (4.13%) | 0.5–1 | 13,513 (44.66%) | ||
High school | 5655 (18.69%) | 1–2 | 6409 (21.18%) | ||
College | 6394 (21.13%) | >2 | 1972 (6.52%) | ||
University | 9362 (30.94%) | Sleep time (hours) | <4 | 471 (1.56%) | |
Graduate school | 5197 (17.18%) | 4–6 | 7375 (24.38%) | ||
Family income (NTD) | Unwaged | 1787 (5.91%) | 6–7 | 14,787 (48.87%) | |
≤200,000 | 2878 (9.51%) | 7–8 | 6499 (21.48%) | ||
200,001–400,000 | NA | 8–9 | NA | ||
400,001–800,000 | 6950 (22.97%) | >9 | NA | ||
800,001–1,200,000 | 8256 (27.29%) | Interval Variable (Unit) | Mean ± SD | ||
1,200,001–1,600,000 | 4008 (13.25%) | Age (y/o) | 47.25 ± 12.41 | ||
1,600,001–2,000,000 | 2601 (8.60%) | Body mass index (kg/m2) | 23.66 ± 3.59 | ||
>2,000,000 | 3775 (12.48%) | Body fat (%) | 26.76 ± 6.86 | ||
Urine protein | none | 29,364 (97.06%) | Waist circumference (cm) | 78.84 ± 10.17 | |
trace (+/−) | 521 (1.72%) | Hip circumference (cm) | 95.37 ± 6.31 | ||
+ | 254 (0.84%) | Waist-to-hip ratio (%) | 0.83 ± 0.08 | ||
++ | 87 (0.29%) | Hemoglobin (g/dL) | 14.14 ± 1.51 | ||
+++ | 29 (0.10%) | Fasting plasma glucose (mg/dL) | 103.2 ± 19.35 | ||
++++ | NA | Triglycerides (mg/dL) | 115.79 ± 89.01 | ||
Current smoker | Never | 22,339 (73.84%) | Total cholesterol (mg/dL) | 196.99 ± 34.40 | |
Passive smoking | 1066 (3.52%) | Free thyroxine 4 (ng/dL) | 1.08 ± 0.15 | ||
Quit | 2450 (8.10%) | Thyroid-stimulating hormone (μIU/mL) | 1.73 ± 1.77 | ||
Occasional | 1062 (3.51%) | C-reactive protein (mg/dL) | 0.21 ± 0.39 | ||
Addicted | 3338 (11.03%) | ||||
Alcohol drinker | Never | 24,832 (82.08%) | Control Variable (Unit) | Mean ± SD | |
Quit | 650 (2.15%) | High-density lipoprotein cholesterol (mg/dL) | 59.01 ± 14.92 | ||
1–2 times a week | 3225 (10.66%) | Low-density lipoprotein cholesterol (mg/dL) | 118.77 ± 32.2 | ||
3–4 times a week | 1045 (3.45%) | Dependent Variable (Unit) | N (%) | ||
5–6 times a week | NA | Hypertension in early stage (HTN) | SBP < 120 and DBP < 80 | 23,180 (76.62%) | |
Addicted | 503 (1.66%) | SBP ≥ 120 and DBP ≥ 80 | 7075 (23.38%) |
Subgroup, Total N = 30,255 | Method | Sensitivity | Specificity | AUC | BA | GM |
---|---|---|---|---|---|---|
IRR–HDL & IRR–LDL (G1) n = 17,327 (57.27%) | SGB | 0.625 | 0.770 | 0.762 | 0.698 | 0.694 |
MARS | 0.659 | 0.732 | 0.762 | 0.695 | 0.694 | |
Lasso | 0.645 | 0.755 | 0.762 | 0.700 | 0.698 | |
Ridge | 0.668 | 0.725 | 0.761 | 0.696 | 0.696 | |
CatBoost | 0.605 | 0.791 | 0.764 | 0.698 | 0.692 | |
IRR–HDL & ORR–LDL (G2) n = 9492 (31.37%) | SGB | 0.595 | 0.715 | 0.705 | 0.655 | 0.652 |
MARS | 0.567 | 0.735 | 0.707 | 0.651 | 0.645 | |
Lasso | 0.691 | 0.617 | 0.705 | 0.654 | 0.653 | |
Ridge | 0.713 | 0.594 | 0.705 | 0.653 | 0.650 | |
CatBoost | 0.682 | 0.613 | 0.703 | 0.648 | 0.647 | |
ORR–HDL & IRR–LDL (G3) n = 2525 (8.35%) | SGB | 0.642 | 0.695 | 0.702 | 0.668 | 0.668 |
MARS | 0.660 | 0.649 | 0.685 | 0.655 | 0.655 | |
Lasso | 0.572 | 0.733 | 0.688 | 0.653 | 0.648 | |
Ridge | 0.583 | 0.718 | 0.687 | 0.650 | 0.647 | |
CatBoost | 0.575 | 0.741 | 0.693 | 0.658 | 0.652 | |
ORR–HDL & ORR–LDL (G4) n = 911 (3.01%) | SGB | 0.581 | 0.702 | 0.658 | 0.642 | 0.639 |
MARS | 0.728 | 0.575 | 0.649 | 0.651 | 0.647 | |
Lasso | 0.706 | 0.596 | 0.653 | 0.651 | 0.649 | |
Ridge | 0.478 | 0.787 | 0.653 | 0.633 | 0.613 | |
CatBoost | 0.456 | 0.809 | 0.650 | 0.632 | 0.607 |
Subgroup | Methods | SGB | MARS | Lasso | Ridge |
---|---|---|---|---|---|
IRR–HDL & IRR–LDL (G1) | SGB | – | |||
MARS | 0.467 | – | |||
Lasso | 0.286 | 0.716 | – | ||
Ridge | 0.164 | 0.517 | 0.085 | – | |
CatBoost | 0.068 | 0.350 | 0.647 | 0.912 | |
IRR–HDL & ORR–LDL (G2) | SGB | – | |||
MARS | 0.643 | – | |||
Lasso | 0.874 | 0.778 | – | ||
Ridge | 0.957 | 0.711 | 0.494 | – | |
CatBoost | 0.589 | 0.410 | 0.588 | 0.664 | |
ORR–HDL & IRR–LDL (G3) | SGB | – | |||
MARS | 0.273 | – | |||
Lasso | 0.319 | 0.857 | – | ||
Ridge | 0.288 | 0.933 | 0.477 | – | |
CatBoost | 0.436 | 0.653 | 0.742 | 0.933 | |
ORR–HDL & ORR–LDL (G4) | SGB | – | |||
MARS | 0.774 | – | |||
Lasso | 0.899 | 0.904 | – | ||
Ridge | 0.906 | 0.910 | 0.992 | – | |
CatBoost | 0.865 | 0.967 | 0.960 | 0.957 |
Rank\Subgroup | IRR–HDL & IRR–LDL (G1) | IRR–HDL & ORR–LDL(G2) | ORR–HDL & IRR–LDL (G3) | ORR–HDL & ORR–LDL (G4) |
---|---|---|---|---|
1 | WHR | BMI | BMI | Hb |
2 | Age | Hb | Hb | CRP |
3 | Hb | TG | WHR | BMI |
4 | BMI | WHR | Age | WC |
5 | FPG | Age | FPG | WHR |
6 | WC | CRP | TG | Age |
7 | FT4 | FPG | WC | HC |
8 | UP | UP | UP | FPG |
9 | AD | WC | FI | ET |
10 | CS | CS | TSH | FT4 |
Subgroup | IRR–HDL & IRR–LDL (G1) | IRR–HDL & ORR–LDL(G2) | ORR–HDL & IRR–LDL (G3) | ORR–HDL & ORR–LDL (G4) |
---|---|---|---|---|
IRR–HDL & IRR–LDL (G1) | 1 | |||
IRR–HDL & ORR–LDL (G2) | 0.622 | 1 | ||
ORR–HDL & IRR–LDL (G3) | 0.633 | 0.899 | 1 | |
ORR–HDL & ORR–LDL (G4) | 0.371 | 0.707 | 0.602 | 1 |
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Liao, P.-C.; Chen, M.-S.; Jhou, M.-J.; Chen, T.-C.; Yang, C.-T.; Lu, C.-J. Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol. Diagnostics 2022, 12, 1965. https://doi.org/10.3390/diagnostics12081965
Liao P-C, Chen M-S, Jhou M-J, Chen T-C, Yang C-T, Lu C-J. Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol. Diagnostics. 2022; 12(8):1965. https://doi.org/10.3390/diagnostics12081965
Chicago/Turabian StyleLiao, Pen-Chih, Ming-Shu Chen, Mao-Jhen Jhou, Tsan-Chi Chen, Chih-Te Yang, and Chi-Jie Lu. 2022. "Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol" Diagnostics 12, no. 8: 1965. https://doi.org/10.3390/diagnostics12081965