Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine
Abstract
:1. Introduction
- The two risk levels of patients with esophageal cancer were divided by five-year survival [21,22]. In this study, the influences of different activation functions on the KELM model were studied, the results are verified theoretically and experimentally, the problem of activation function selection in the survival risk prediction model of esophageal cancer based on the KELM was solved.
2. Materials and Methods
2.1. Data Sources
2.2. Kohonen Clustering Network
- The network consists of two layers, namely an input layer and an output layer. The output layer is also called the competition layer and does not include the hidden layer.
- Each input node in the input layer is wholly connected to the output node.
- The output nodes are distributed in a two-dimensional structure, and there are lateral connections between the nodes.
- Step 1: Data normalization.
- Step 2: Determine the initial center point of the cluster.
- Step 3: Calculate the distance [29].
- Step 4: Adjust the position.
- Step 5: Determine whether the conditions for the end of the iteration are met.
2.3. Kernel Extreme Learning Machine (KELM)
3. Optimized Kernel Extreme Learning Machine
3.1. Particle Swarm Optimization to Optimize the KELM
3.2. Particle Swarm Optimization Algorithm Based on Competitive Learning (CLPSP) to Optimize the KELM
3.3. Sparrow Search Algorithm (SSA) to Optimize the KELM
- Under normal circumstances, the discoverer has a relatively high energy reserve, and the energy reserve corresponds to adaptability.
- The identity between the discoverer and the joiner changes dynamically, and their ratio remains unchanged.
- The position of the joiner is proportional to the energy, the lower the power, the more likely it is to fly to other places for food.
- Joiners will always find discoverers who provide good food and fight with them.
- After the alarm value is greater than the safe value, the discoverer will lead the sparrow population into the safe area.
- When the entire population moves, the sparrows at the edge of the people will quickly move to a safe place. In contrast, the sparrows inside the population will randomly move to the surrounding sparrows. The location update of the discoverer
3.4. Adaptive Mutation Sparrow Search Algorithm (AMSSA) to Optimize the KELM
4. Result
4.1. Data Clustering
4.2. Choice of Kernel Function of the KELM
4.3. Optimization Algorithm Optimization Results
4.4. Results Predicted by Different Models
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Expressions | Parameters |
---|---|---|
Radial Basis Kernel | : Free parameters. | |
Linear Kernel | : Constant | |
Polynomial Kernel | : Slope : Constant : Number of polynomials | |
Wavelet Kernel | : Wavelet expansion factor : Translation factor |
Classification category | 1 | 2 | 3 | 4 | 5 |
Number of samples | 82 | 76 | 53 | 50 | 79 |
Kernel Function Type | Risk Level | (%) | (%) | ||||
---|---|---|---|---|---|---|---|
Radial basis function | Level 1 | 70.0 | 68.5 | 70.0 | 66.7 | 33.3 | 30.0 |
Level 2 | 66.7 | ||||||
Linear | level 1 | 60.0 | 53.4 | 60.0 | 45.5 | 54.5 | 40.0 |
level 2 | 45.5 | ||||||
Polynomial | level 1 | 65.0 | 54.8 | 65.0 | 42.4 | 57.6 | 35.0 |
level 2 | 42.4 | ||||||
Wavelet | level 1 | 37.5 | 43.8 | 37.5 | 51.5 | 48.5 | 62.5 |
level 2 | 51.5 |
Predictive Model | Risk Level | (%) | (%) | Running Time(s) | ||||
---|---|---|---|---|---|---|---|---|
KELM | level 1 | 70.0 | 68.5 | 70 | 66.7 | 33.3 | 30.0 | 3.20 |
level 2 | 66.7 | |||||||
SSA-KELM | level 1 | 90.0 | 89.0 | 90 | 87.8 | 12.1 | 10.0 | 15.38 |
level 2 | 87.8 | |||||||
PSO-KELM | level 1 | 92.5 | 84.9 | 93 | 75.6 | 24.2 | 7.5 | 17.56 |
level 2 | 75.6 | |||||||
CLPSO-KELM | level 1 | 95.0 | 89.0 | 95 | 81.8 | 18.2 | 5 | 14.12 |
level 2 | 81.8 | |||||||
AMSSA-KELM | level 1 | 95.0 | 91.8 | 95 | 87.8 | 12.1 | 5 | 10.26 |
level 2 | 87.8 |
Predictive Model | Risk Level | (%) | (%) | Running Time(s) | ||||
---|---|---|---|---|---|---|---|---|
AMSSA-KELM | level 1 | 95.0 | 91.8 | 95.0 | 87.8 | 12.1 | 5.0 | 10.26 |
level 2 | 87.8 | |||||||
ABC-SVM | level 1 | 87.5 | 81.8 | 87.5 | 72.7 | 27.3 | 12.5 | 10.38 |
level 2 | 72.7 | |||||||
TLRF | level 1 | 57.5 | 61.6 | 57.5 | 66.7 | 33.3 | 42.5 | 20.15 |
level 2 | 66.7 | |||||||
GP-SVM | level 1 | 90.0 | 83.6 | 90.0 | 75.6 | 24.2 | 10.0 | 30.50 |
level 2 | 75.6 | |||||||
Cox-LMM | level 1 | 62.5 | 61.6 | 62.5 | 60.6 | 39.4 | 37.5 | 15.41 |
level 2 | 60.6 |
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Wang, Y.; Wang, H.; Li, S.; Wang, L. Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine. Mathematics 2022, 10, 1367. https://doi.org/10.3390/math10091367
Wang Y, Wang H, Li S, Wang L. Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine. Mathematics. 2022; 10(9):1367. https://doi.org/10.3390/math10091367
Chicago/Turabian StyleWang, Yanfeng, Haohao Wang, Sanyi Li, and Lidong Wang. 2022. "Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine" Mathematics 10, no. 9: 1367. https://doi.org/10.3390/math10091367
APA StyleWang, Y., Wang, H., Li, S., & Wang, L. (2022). Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine. Mathematics, 10(9), 1367. https://doi.org/10.3390/math10091367