# Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Kernel Method

#### 2.1.1. Linear Kernel

#### 2.1.2. Polynomial Kernel

#### 2.1.3. Radial Basis Function (RBF) Kernel

#### 2.2. Twin Support Vector Machine (TWSVM)

#### 2.3. k-Fold Cross Validation

#### 2.4. Proposed Method

## 3. Results and Discussions

#### 3.1. Data

#### 3.2. Confusion Matrix

- TP = Many cases of pancreatic cancer are predicted to be correct
- TN = Many cases of not pancreatic cancer are predicted to be correct
- FP = Many cases of not pancreatic cancer are predicted to be wrong (predicted as pancreatic cancer)
- FN = Many pancreatic cancer cases are predicted to be wrong (predicted as not pancreatic cancer)

#### 3.3. Evaluation Parameters

#### 3.4. Results

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Illustration of the twin support vector machine (TWSVM) [20].

No | CA (U/mL) | Hemoglobin (g/dL) | Leukocytes (sel/uL) | Hematocrit (%) | Platelets (sel/uL) | Diagnosis |
---|---|---|---|---|---|---|

1 | 5.73 | 12.1 | 10,200 | 36.7 | 143,000 | N |

2 | 8.05 | 11.8 | 11,300 | 35.9 | 222,000 | N |

3 | 86.21 | 10.1 | 12,800 | 34.1 | 346,000 | Y |

4 | 87.13 | 12 | 11,700 | 36.7 | 612,000 | Y |

Predict | |||
---|---|---|---|

Pancreatic Cancer (Y) | Not Pancreatic Cancer (N) | ||

Actual | Pancreatic Cancer (Y) | True Positive (TP) | False Negative (FN) |

Not Pancreatic Cancer (N) | False Positive (FP) | True Negative (TN) |

Parameter | Formula | Explanation |
---|---|---|

Accuracy | $\frac{\left(\mathrm{TN}+\mathrm{TP}\right)}{\left(\mathrm{FN}+\mathrm{TP}+\mathrm{FP}+\mathrm{TN}\right)}\times 100\%$ | Comparison between the number of cases of pancreatic cancer and not pancreatic cancer that identified correctly with the total number of all cases |

Sensitivity | $\frac{\mathrm{TP}}{\left(\mathrm{FN}+\mathrm{TP}\right)}\times 100\%$ | Proportion of pancreatic cancer cases identified correctly |

Specificity | $\frac{\mathrm{TN}}{\left(\mathrm{FP}+\mathrm{TN}\right)}\times 100\%$ | Proportion of not pancreatic cancer cases identified correctly |

Classification Model | Accuracy (%) | Sensitivity (%) | Specificity (%) | Running Time (seconds) |
---|---|---|---|---|

TWSVM with Linear Kernel | 92% | 86% | 95% | 1.2811 |

TWSVM with Polynomial Kernel with $\mathrm{d}\text{}=4$ | 80% | 75% | 83% | 1.2040 |

TWSVM with RBF Kernel with $\mathsf{\sigma}\text{}=0.05$ | 98% | 97% | 100% | 1.3408 |

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## Share and Cite

**MDPI and ACS Style**

Sadewo, W.; Rustam, Z.; Hamidah, H.; Chusmarsyah, A.R.
Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel. *Symmetry* **2020**, *12*, 667.
https://doi.org/10.3390/sym12040667

**AMA Style**

Sadewo W, Rustam Z, Hamidah H, Chusmarsyah AR.
Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel. *Symmetry*. 2020; 12(4):667.
https://doi.org/10.3390/sym12040667

**Chicago/Turabian Style**

Sadewo, Wismaji, Zuherman Rustam, Hamidah Hamidah, and Alifah Roudhoh Chusmarsyah.
2020. "Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel" *Symmetry* 12, no. 4: 667.
https://doi.org/10.3390/sym12040667