An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM
Abstract
:1. Introduction
2. Materials and Methods
2.1. Prediction Methods for Equipment Power
2.2. TCN-BiLSTM
2.3. SSA
2.4. Improved SSA
3. Experimental and Results Analysis
3.1. Explanation of Experimental Data
3.2. Data Preprocessing
3.2.1. Anomaly Data Processing
3.2.2. Data Normalization
3.3. Model Evaluation Metrics
3.4. Experiment Platform and Training Hyperparameters
3.5. Model Comparison Analysis
4. Discussion
5. Summary
- (1)
- Through Pearson correlation analysis, abnormal data processing, and normalization, the original sample set is preprocessed to extract useful features related to active power changes. This process enhances the correlation between input features, addressing the limitations of low input quality and high information redundancy in traditional prediction models.
- (2)
- The use of TCN to extract and reduce key input features has improved the accuracy of BiLSTM. In addition, by improving the global search and local search capabilities of the SSA, the parameter selection of TCN-BiLSTM is optimized, aiming to avoid blind parameter settings and getting stuck in local optimal solutions. Verified by actual measurement data, the model proposed in this article can effectively improve prediction accuracy, thereby achieving reliable prediction of active power for different medical equipment.
- (3)
- When using different evaluation criteria to measure model performance, some potential influencing factors are sometimes ignored. Therefore, in the final evaluation step of the model prediction performance, we selected the three evaluation criteria of RMSE, MAE, and R2 to evaluate the accuracy of the model. At the same time, RT was used to evaluate the complexity of the model by the speed of the model response. Through comparative experiments, when the ISSA-TCN-BiLSTM model proposed in this paper is applied to MRI and CT equipment, its RMSE, MAE, and R2 values show that its prediction performance is better than LSTM, GRU, and TCN-BiLSTM models, while the RT index is slightly inferior to LSTM and GRU, but in a large data set, this impact is small. Combining these four evaluation criteria helps to obtain a more objective and effective evaluation of the model proposed in this paper.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Input Vector | Data Features |
---|---|
Active power | The active power value of the medical equipment at the sampling time point, expressed in KW. |
Frequency of use | Record of the frequency of the use of medical equipment, expressed as number of times. |
Operational status | Record of the current operating state of the equipment. MRI equipment and CT equipment are divided into standby state and running state. |
Temperature | The temperature change in the equipment room affects the stability of medical devices. |
Humidity | The humidity change in the equipment room affects the stability of medical devices. |
Equipment cleanliness | The cleanliness index of medical equipment is evaluated by scoring. |
Maintenance frequency | Record the maintenance times of the medical equipment. Maintenance of medical equipment can improve the operation stability. |
Equipment parameters | Includes models of medical equipment (MRI and CT) and power levels. |
Configuration | Parameter |
---|---|
CPU | Inter(R) Xeon(R) W-2223 (Inter, Santa Clara, CA, USA) |
GPU | Nvidia GeForce RTX 2080ti (Nvidia, Santa Clara, CA, USA) |
Operating system | Windows 10 |
Interpreter setting | Python 3.8 and torch 1.13.1 |
Parameter | Value |
---|---|
Population size | 50 |
Discoverer | 20% |
Followers | 80% |
Initial alerter | 10% |
Iterations | 300 |
Model | RMSE | MAE | R2 | RT | ||||
---|---|---|---|---|---|---|---|---|
MRI | CT | MRI | CT | MRI | CT | MRI | CT | |
1 | 0.2435 | 0.2978 | 0.2246 | 0.2811 | 0.74 | 0.71 | 3.5456 | 3.9512 |
2 | 0.2105 | 0.2231 | 0.1873 | 0.1689 | 0.83 | 0.85 | 3.8954 | 4.1023 |
3 | 0.1925 | 0.1974 | 0.1561 | 0.1421 | 0.91 | 0.90 | 4.5489 | 4.6127 |
4 | 0.1143 | 0.1157 | 0.0873 | 0.0817 | 0.95 | 0.96 | 3.9864 | 4.1712 |
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Lin, Z.; Ji, Z. An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM. Sensors 2024, 24, 5589. https://doi.org/10.3390/s24175589
Lin Z, Ji Z. An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM. Sensors. 2024; 24(17):5589. https://doi.org/10.3390/s24175589
Chicago/Turabian StyleLin, Zicong, and Zhiyong Ji. 2024. "An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM" Sensors 24, no. 17: 5589. https://doi.org/10.3390/s24175589