# Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Arrhythmia Dataset

#### 2.2. Proposed Method

#### 2.2.1. Principal Component Analysis (PCA)

#### 2.2.2. Quantum Support Vector Machine (QSVM)

#### 2.2.3. Experimental Setups

## 3. Experimental Results

#### 3.1. Scenario 1: Different Number of Qubits

#### 3.2. Scenario 2: Different Amount of Data

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**(

**a**) The performance of data cases according to 3 qubits. (

**b**) The performance of data cases according to 5 qubits. (

**c**) The performance of data cases according to 7 qubits. (

**d**) The performance of data cases according to 9 qubits.

**Figure 5.**Confusion matrices obtained on test data for QSVM and SVM: (

**a**) confusion matrix for QSVM, (

**b**) confusion matrix for SVM.

**Figure 6.**Confusion matrices obtained with quantum support vector machine (QSVM) classifier: (

**a**) highest and (

**b**) lowest accuracy.

**Figure 7.**QSVM and SVM performances with different qubit numbers according to Data Case 1 (

**a**), Data Case 2 (

**b**), Data Case 3 (

**c**), Data Case 4 (

**d**), Data Case 6 (

**e**), Data Case 6 (

**f**), and Data Case 7 (

**g**).

Merged Rhythms | New Class |
---|---|

AF + AFIB | AFIB |

SVT + AT + SAAWR + SINT + AVNRT + AVRT | GSVT |

SB | SB |

SR + SI | SR |

Method | Amount of Data | ||||||
---|---|---|---|---|---|---|---|

Data Case 1 | Data Case 2 | Data Case 3 | Data Case 4 | Data Case 5 | Data Case 6 | Data case 7 | |

209 | 418 | 625 | 800 | 1031 | 1534 | 3133 | |

Qubit = 3 | |||||||

SVM | 65.09 ± 6.03 | 70.29 ± 2.95 | 72.14 ± 2.61 | 72.56 ± 2.60 | 77.23 ± 2.58 | 73.25 ± 2.17 | 74.50 ± 0.86 |

QSVM | 59.51 ± 7.06 | 66.85 ± 5.08 | 68.73 ± 2.62 | 69.40 ± 2.56 | 73.88 ± 3.15 | 72.73 ± 2.26 | 74.46 ± 1.27 |

Qubit = 5 | |||||||

SVM | 69.03 ± 6.12 | 72.94 ± 3.73 | 74.90 ± 3.11 | 75.32 ± 2.44 | 77.29 ± 3.58 | 76.51 ± 2.05 | 77.94 ± 1.21 |

QSVM | 62.78 ± 4.98 | 67.72 ± 5.28 | 71.46 ± 2.74 | 72.36 ± 2.86 | 74.66 ± 4.04 | 75.54 ± 1.61 | 78.06 ± 1.69 |

Qubit = 7 | |||||||

SVM | 69.23 ± 5.28 | 76.42 ± 4.60 | 78.11 ± 3.79 | 78.57 ± 2.23 | 79.12 ± 2.40 | 80.39 ± 1.84 | 81.95 ± 1.19 |

QSVM | 65.76 ± 5.17 | 69.17 ± 3.75 | 75.45 ± 3.09 | 76.62 ± 2.59 | 76.17 ± 2.96 | 79.13 ± 1.72 | 81.82 ± 1.47 |

Qubit = 9 | |||||||

SVM | 71.93 ± 4.04 | 78.59 ± 4.35 | 79.70 ± 3.21 | 79.92 ± 2.16 | 80.00 ± 2.18 | 81.40 ± 1.51 | 83.17 ± 1.38 |

QSVM | 71.05 ± 6.10 | 69.32 ± 3.44 | 78.73 ± 2.94 | 77.21 ± 3.31 | 79.44 ± 2.87 | 79.53 ± 1.61 | 82.73 ± 1.13 |

Qubit = 11 | |||||||

SVM | 74.23 ± 4.87 | 78.16 ± 3.87 | 79.70 ± 3.21 | 80.37 ± 2.16 | 80.87 ± 2.24 | 81.52 ± 1.61 | - |

QSVM | 72.69 ± 6.10 | 72.12 ± 3.10 | 78.73 ± 2.94 | 79.35 ± 1.86 | 77.98 ± 1.86 | 79.70 ± 1.90 | - |

**Table 3.**Comparisons of quantum support vector machine (QSVM) and support vector machine (SVM) classifier performance metrics, detailed.

Classifier | Performance Metrics (%) | ||||
---|---|---|---|---|---|

Class | Precision | Sensitivity | Specificity | F1-Score | |

QSVM | AFIB | 61.53 | 50.52 | 92.82 | 55.48 |

GSVT | 77.41 | 82.05 | 92.83 | 79.66 | |

SB | 90.22 | 97.82 | 93.98 | 93.86 | |

SR | 81.69 | 77.67 | 95.07 | 79.62 | |

SVM | AFIB | 57.25 | 37.36 | 91.92 | 45.21 |

GSVT | 71.85 | 82.90 | 87.58 | 76.98 | |

SB | 89.62 | 98.64 | 91.21 | 93.91 | |

SR | 6.38 | 5.55 | 94.44 | 5.93 |

Classifier | Performance Metrics (%) | ||||
---|---|---|---|---|---|

Class | Precision | Sensitivity | Specificity | F1-Score | |

QSVM(H) | AFIB | 75.34 | 53.65 | 95.56 | 62.67 |

GSVT | 77.73 | 90.05 | 93.15 | 83.43 | |

SB | 95.51 | 100.00 | 96.89 | 97.70 | |

SR | 79.39 | 81.44 | 95.01 | 80.40 | |

QSVM(L) | AFIB | 38.09 | 38.09 | 84.33 | 38.08 |

GSVT | - | 0 | - | 84.61 | |

SB | 55.71 | 97.50 | 51.56 | 70.90 | |

SR | 23.07 | 11.11 | 87.01 | 14.99 |

Reference | Classifier | Accuracy (%) | F1—Score | Sensitivity | Specificity |
---|---|---|---|---|---|

Aziz et al. [29] | SVM MLP | 84.2 90.7 | - - | - - | - - |

Sepahvand et al. [30] | Teacher Model CNN Student Model CNN | 98.96 98.13 | 98.65 96.47 | 98.01 95.82 | 98.00 97.86 |

Faust et al. [31] | ResNet | 99.98 | - | 99.94 | 100.00 |

Dhananjay et al. [32] | SVM CatBoost | 71.00 99.00 | 66.11 99.00 | 72.50 99.17 | - - |

Murat et al. [18] | K-NN | 80.94 | 77.92 | 78.03 | 93.75 |

SVM | 84.06 | 80.49 | 81.13 | 94.77 | |

RF | 90.30 | 88.52 | 88.65 | 96.86 | |

NB | 79.90 | 75.71 | 76.42 | 93.38 | |

GBC | 87.68 | 85.21 | 85.53 | 96.03 | |

ABC | 77.27 | 72.81 | 73.36 | 92.72 | |

DTC | 85.78 | 83.46 | 83.54 | 95.41 | |

MLP | 77.71 | 74.20 | 75.34 | 92.76 | |

QDA | 77.01 | 72.79 | 73.62 | 92.44 | |

Baygin et al. [33] | SVM | 97.18 | - | - | - |

Proposed Method | SVM QSVM | 86.96 84.64 | 82.41 81.15 | 81.70 81.13 | 95.61 95.00 |

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**MDPI and ACS Style**

Ozpolat, Z.; Karabatak, M.
Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification. *Diagnostics* **2023**, *13*, 1099.
https://doi.org/10.3390/diagnostics13061099

**AMA Style**

Ozpolat Z, Karabatak M.
Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification. *Diagnostics*. 2023; 13(6):1099.
https://doi.org/10.3390/diagnostics13061099

**Chicago/Turabian Style**

Ozpolat, Zeynep, and Murat Karabatak.
2023. "Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification" *Diagnostics* 13, no. 6: 1099.
https://doi.org/10.3390/diagnostics13061099