# Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

^{1}

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## Abstract

**:**

## 1. Introduction

- (a)
- A spatial transformation improved SSA (STSSA) is applied to KELM training.
- (b)
- The established STSSA effectively tackled the parameter turning for KELM in an excellent manner.
- (c)
- The developed STSSA-KELM model is mainly applied to predict coronary heart disease.

## 2. Materials and Methods

#### 2.1. DATA Collection

#### 2.2. Proposed Stssa-Kelm Method

#### 2.2.1. Parameter Optimization and Feature Selection by Continuous and Binary STSSA

#### 2.2.2. Classification Based on KELM

#### 2.2.3. Detailed Procedure of STSSA-KELM

- Step 1:
- Initialize the parameters of STSSA: the maximum number of iterations T, the number of search agents N.
- Step 2:
- Initialize the search agents of STSSA. Use random numbers generated in the solution space to initialize continuous variables in the search agent, and use random 0 or 1 to initialize discrete variables.
- Step 3:
- Calculate the fitness value of each search agent, according to the following formula:

- Step 4:
- Perform spatial transformation mechanism and select the highest fitness N search agent updates the current population.
- Step 5:
- Update parameter c, according to Equation (2).

- Step 6:
- Update the value of the search agents.
- Step 7:
- If the maximum iterations are satisfied, output the best search agent where the first two dimensions represent (C, γ), and the binary values of the other dimensions are used to filter out the selected features. Otherwise, jump to Step 4.
- Step 8:
- Optimize the obtained optimal parameters and optimal feature subsets with the KELM prediction model, and use the optimal model to predict the test set.
- Step 9:
- If the termination condition is met, output the average result. Otherwise, jump to Step 8.

## 3. Results

^{−10}, 2

^{10}]. For RF, the default setting was used.

## 4. Discussion

## 5. Conclusions and Future Perspectives

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Classification performance of five models, in terms of ACC, MCC, sensitivity, and specificity.

**Figure 3.**Relationship between training accuracy of STSSA-KELM, GWO-KELM, SSA-KELM, and PSO-KELM and the number of iterations.

No. | Feature | Detailed Description |
---|---|---|

F1 | Age | Normal group (X, SD) = 60.04 ± 10.85 CHD (Coronary Heart Disease) group (X, SD) = 64.08 ± 9.71 |

F2 | Marriage | Unmarried = 0; Married = 1; Divorce = 2 |

F3 | Gender | Female = 0; Male = 1 |

F4 | Weight (kg) | Normal group (X, SD) = 65.62 ± 10.62 CHD group (X, SD) = 65.89 ± 11.07 |

F5 | Height (cm) | Normal group (X, SD) = 165.26 ± 7.48 CHD group (X, SD) = 164.15 ± 8.22 |

F6 | Systolic blood pressure (SBP) (mmHg) | Normal group (X, SD) = 132.93 ± 18.15 CHD group (X, SD) = 134.36 ± 17.39 |

F7 | Diastolic blood pressure (DBP) (mmHg) | Normal group (X, SD) = 78.69 ± 12.32 CHD group (X, SD) = 77.67 ± 12.14 |

F8 | Heart rate (HR) (/min) | Normal group (X, SD) = 74.13 ± 12.98 CHD group (X, SD) = 73.78 ± 12.60 |

F9 | Hypertension (HBP) | No = 0; Yes = 1 |

F10 | Hyperlipidemia (HPA) | No = 0; Yes = 1 |

F11 | Diabetes mellitus (DM) | No = 0; Yes = 1 |

F12 | Renal insufficiency/renal failure (RF) | No = 0; Yes = 1 |

F13 | History of mental illness (HMI) | No = 0; Yes = 1 |

F14 | History of vascular diseases (HVD) | No = 0; Yes = 1 |

F15 | History of pulmonary diseases (HPD) | No = 0; Yes = 1 |

F16 | Smoking history (SH) | No = 0; Yes = 1 |

F17 | Drinking history (DH) | No = 0; Yes = 1 |

F18 | Hemoglobin (HB) (g/L) | Normal group (X, SD) = 138.18 ± 16.13 CHD group (X, SD) = 135.77 ± 16.37 |

F19 | White blood cell count (WBC) (/L) | Normal group (X, SD) = 6.46 ± 1.78 CHD group (X, SD) = 6.62 ± 1.20 |

F20 | Platelet count (Plt) (/L) | Normal group (X, SD) = 205.94 ± 58.37 CHD group (X, SD) = 202.75 ± 55.69 |

F21 | Sodium (Na) (mmol/L) | Normal group (X, SD) = 140.99 ± 2.18 CHD group (X, SD) = 141.12 ± 2.25 |

F22 | Potassium (K) (mmol/L) | Normal group (X, SD) = 4.53 ± 9.15 CHD group (X, SD) = 3.99 ± 0.33 |

F23 | Calcium (Ca) (mmol/L) | Normal group (X, SD) = 2.81 ± 6.95 CHD group (X, SD) = 2.31 ± 0.44 |

F24 | Blood glucose (Glu) (mmol/L) | Normal group (X, SD) = 5.71 ± 2.88 CHD group (X, SD) = 5.73 ± 5.41 |

F25 | Creatinine (Cr) (umol/L) | Normal group (X, SD) Normal group (X, SD) = 67.15 ± 42.46 CHD group (X, SD) = CHD group (X, SD) = 67.65 ± 19.26 |

F26 | Estimated glomerular filtration rate (eGFR) | Normal group (X, SD) = 116.65 ± 30.84 CHD group (X, SD) = 130.79 ± 33.88 |

F27 | Blood urea nitrogen (BUN) (mmol/L) | Normal group (X, SD) = 5.57 ± 1.68 CHD group (X, SD) = 5.92 ± 1.75 |

F28 | Brain natriuretic factor or peptide (BNP) (pg/mL) | Normal group (X, SD) = 144.77 ± 539.80 CHD group (X, SD) = 151.07 ± 433.61 |

F29 | Total cholesterol (TC) (mmol/L) | Normal group (X, SD) = 4.22 ± 1.07 CHD group (X, SD) = 4.28 ± 1.13 |

F30 | Triglyceride (TG) (mmol/L) | Normal group (X, SD) = 1.67 ± 0.92 CHD group (X, SD) = 1.59 ± 0.99 |

F31 | High-density lipoprotein cholesterol (HDL) (mmol/L) | Normal group (X, SD) = 1.20 ± 0.27 CHD group (X, SD) = 1.19 ± 0.26 |

F32 | Low-density lipoprotein cholesterol (LDL) (mmol/L) | Normal group (X, SD) = 2.25 ± 0.85 CHD group (X, SD) = 2.32 ± 0.81 |

F33 | Thyrotropin (TSH) (mmol/L) | Normal group (X, SD) = 2.11 ± 0.45 CHD group (X, SD) = 2.75 ± 2.71 |

F34 | Left ventricular ejection fraction (LVEF) (%) | Normal group (X, SD) = 64.85 ± 9.48 CHD group (X, SD) = 65.39 ± 9.48 |

**Table 2.**Classification performance of STSSA-KELM, in terms of ACC (Accuracy), MCC (Matthews correlation coefficient), sensitivity, and specificity.

Fold | ACC | MCC | Sensitivity | Specificity |
---|---|---|---|---|

#1 | 0.9550 | 0.9130 | 1.0000 | 0.9130 |

#2 | 0.8000 | 0.6010 | 0.8180 | 0.7830 |

#3 | 0.8000 | 0.6010 | 0.8180 | 0.7830 |

#4 | 0.8670 | 0.7370 | 0.9090 | 0.8260 |

#5 | 0.8410 | 0.6830 | 0.8180 | 0.8640 |

#6 | 0.9090 | 0.8220 | 0.9550 | 0.8640 |

#7 | 0.8180 | 0.6360 | 0.8180 | 0.8180 |

#8 | 0.7500 | 0.5050 | 0.8180 | 0.6820 |

#9 | 0.8410 | 0.6830 | 0.8180 | 0.8640 |

#10 | 0.8640 | 0.7400 | 0.9550 | 0.7730 |

Mean | 0.8440 | 0.6920 | 0.8730 | 0.8170 |

STD. | 0.0580 | 0.1180 | 0.0740 | 0.0650 |

Algorithm | STSSA-KELM | SSA-KELM | PSO-KELM | GWO-KELM | SVM | RF |
---|---|---|---|---|---|---|

Mean-level | 1.5 | 3 | 4.75 | 5 | 2.5 | 4.25 |

Rank | 1 | 3 | 5 | 6 | 2 | 4 |

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**MDPI and ACS Style**

He, W.; Xie, Y.; Lu, H.; Wang, M.; Chen, H.
Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm. *Symmetry* **2020**, *12*, 1651.
https://doi.org/10.3390/sym12101651

**AMA Style**

He W, Xie Y, Lu H, Wang M, Chen H.
Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm. *Symmetry*. 2020; 12(10):1651.
https://doi.org/10.3390/sym12101651

**Chicago/Turabian Style**

He, Wenming, Yanqing Xie, Haoxuan Lu, Mingjing Wang, and Huiling Chen.
2020. "Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm" *Symmetry* 12, no. 10: 1651.
https://doi.org/10.3390/sym12101651