Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics
Abstract
:1. Introduction
2. Methodology
2.1. Basics of the Inverse Problem Algorithm
2.2. The IPA Flowchart
2.3. Semi-Empirical Formula Elaboration
2.4. SYNTAX Score and Seven Risk Factors
2.5. Running STATISTICA 7.0 Program
3. Results
3.1. STATISTICA 7.0 Outcomes
3.2. Quantified Performance
4. Discussion
4.1. Verifying the Predicted SYNTAX Score
4.2. Dominant Factors of the SYNTAX Score Prediction
4.3. Reducing the Number of Risk Factors
4.4. Discussion of Similar Research Results Based on Various Risk Factors
4.5. IPA Technique in Artificial Intelligence Applications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Factor | Range | Derived Data | |||
---|---|---|---|---|---|
Case No./Max. | Case No./Min. | Mean | Median | St. Dev | |
Age (yr) | 75/98 | 71/29 | 70 | 71 | 14.1 |
MAP (mmHg) | 386/153 | 276/50 | 101 | 101 | 18 |
BSA (m2) | 264/2.40 | 63/1.09 | 1.70 | 1.70 | 0.20 |
Glucose AC (mg/dL) | 15/689 | 272/54 | 156.6 | 126.5 | 87.4 |
LDL-C (mg/dL) | 125/283 | 92/12 | 100.4 | 98.0 | 39.5 |
cTnI (ng/mL) | 224/102.98 | 265/0.01 | 5.09 | 0.22 | 16.67 |
CRP (mg/dL) | 193/42.50 | 368/0.01 | 5.51 | 2.47 | 7.19 |
SYNTAX score | 191/41 | 267/5 | 20.8 | 21.0 | 4.9 |
Factor | Range after Normalized | Derived Data after Normalized | |||
---|---|---|---|---|---|
Case No./Max. | Case No./Min. | Mean | Median | St. Dev | |
Age (yr) | 75/+1 | 71/−1 | 0.17 | 0.22 | 0.41 |
MAP (mmHg) | 386/+1 | 276/−1 | 0.00 | 0.00 | 0.36 |
BSA (m2) | 264/+1 | 63/−1 | 0.07 | 0.07 | 0.31 |
Glucose AC (mg/dL) | 15/+1 | 272/−1 | 0.68 | 0.77 | 0.28 |
LDL-C (mg/dL) | 125/+1 | 92/−1 | 0.35 | 0.37 | 0.29 |
cTnI (ng/mL) | 224/+1 | 265/−1 | 0.90 | 1.00 | 0.32 |
CRP (mg/dL) | 193/+1 | 368/−1 | 0.74 | 0.88 | 0.34 |
SYNTAX score | 191/+1 | 267/−1 | 0.11 | 0.10 | 1.00 |
Biological Index | Factor | Coefficient | After Normalization | |
---|---|---|---|---|
Value | Rank | |||
Age | A | a1 | 0.912696 | 3 |
MAP | B | a2 | 0.618889 | 8 |
BSA | C | a3 | 0.831829 | 5 |
Glucose AC | D | a4 | 0.825880 | 6 |
LDL-C | E | a5 | 1.175788 | 2 |
cTnI | F | a6 | 0.202330 | 20 |
CRP | G | a7 | −0.103741 | 27 |
Age × MAP | A × B | a8 | 0.183358 | 21 |
Age × BSA | A × C | a9 | −0.169841 | 22 |
Age × Glucose AC | A × D | a10 | −0.304887 | 16 |
Age × LDL-C | A × E | a11 | −0.295175 | 17 |
Age × cTnI | A × F | a12 | −0.145316 | 24 |
Age × CRP | A × G | a13 | 1.252458 | 1 |
MAP × BSA | B × C | a14 | −0.219970 | 18 |
MAP × Glucose AC | B × D | a15 | 0.376565 | 13 |
MAP × LDL-C | B × E | a16 | 0.757553 | 7 |
MAP × cTnI | B × F | a17 | −0.135202 | 25 |
MAP × CRP | B × G | a18 | 0.398636 | 11 |
BSA × Glucose AC | C × D | a19 | 0.532427 | 9 |
BSA × LDL-C | C × E | a20 | 0.401664 | 10 |
BSA × cTnI | C × F | a21 | 0.124982 | 26 |
BSA × CRP | C × G | a22 | 0.021501 | 29 |
Glucose AC × LDL-C | D × E | a23 | 0.338031 | 15 |
Glucose AC × cTnI | D × F | a24 | 0.155761 | 23 |
Glucose AC × CRP | D × G | a25 | 0.202936 | 19 |
LDL-C × cTnI | E × F | a26 | 0.873143 | 4 |
LDL-C × CRP | E × G | a27 | −0.347644 | 14 |
cTnI × CRP | F × G | a28 | 0.067604 | 28 |
Constant | a29 | 0.378574 | 12 |
Factor | Range | Derived Data | |||
---|---|---|---|---|---|
Case No./Max. | Case No./Min. | Mean | Median | St. Dev | |
Age (yr) | 88/88 | 6/34 | 66 | 69 | 12.0 |
MAP (mmHg) | 7/148 | 6/60 | 100 | 99 | 17.0 |
BSA (m2) | 89/4.47 | 39/1.30 | 1.76 | 1.71 | 0.32 |
Glucose AC (mg/dL) | 74/385 | 85/66 | 127.2 | 109.6 | 54.9 |
LDL-C (mg/dL) | 84/187 | 95/45 | 104.9 | 99.9 | 41.5 |
cTnI (ng/mL) | 74/102.98 | 1/0.01 | 3.69 | 0.09 | 14.74 |
CRP (mg/dL) | 63/35.72 | 82/0.03 | 6.69 | 2.68 | 7.81 |
SYNTAX score | 87/32 | 7/3 | 20.8 | 21.6 | 5.0 |
Number of Factors | Number of Terms in the Regression Equation | Loss Function, Φ | Variance of Regression, s2 | Linear Regression y = Ax + B | Correlation Coefficient, r2 |
---|---|---|---|---|---|
7 | 29 | 3.1772 | 0.8958 | 0.8958x + 2.1661 | 0.8958 |
6 | 22 | 9.6870 | 0.6822 | 0.6822x + 6.6042 | 0.6822 |
5 | 16 | 11.6247 | 0.6186 | 0.6185x + 7.9252 | 0.6186 |
4 | 11 | 16.1963 | 0.4687 | 0.4687x + 11.041 | 0.4687 |
3 | 7 | 20.1177 | 0.3400 | 0.3400x + 13.715 | 0.3400 |
2 | 4 | 21.5750 | 0.2922 | 0.2922x + 14.709 | 0.2922 |
1 | 2 | 28.5206 | 0.6437 | 0.0644x + 19.444 | 0.0644 |
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Lin, M.-C.; Tseng, V.S.; Lin, C.-S.; Chiu, S.-W.; Pan, L.-K.; Pan, L.-F. Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics. Diagnostics 2022, 12, 3180. https://doi.org/10.3390/diagnostics12123180
Lin M-C, Tseng VS, Lin C-S, Chiu S-W, Pan L-K, Pan L-F. Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics. Diagnostics. 2022; 12(12):3180. https://doi.org/10.3390/diagnostics12123180
Chicago/Turabian StyleLin, Meng-Chiung, Vincent S. Tseng, Chih-Sheng Lin, Shao-Wen Chiu, Lung-Kwang Pan, and Lung-Fa Pan. 2022. "Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics" Diagnostics 12, no. 12: 3180. https://doi.org/10.3390/diagnostics12123180