# Evaluation of the Effect of Integrated Care on Preoperative Anxiety in Day Surgery Patients Based on the MVPA Framework

Authors: Jinhui Chen; Hua Cai; Jia Chen; Lin Peng; Xiaolan Zuo; Runa Li; Lijun Tang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Objective. To investigate the effect of receiving comprehensive care on preoperative anxiety in patients admitted to the operating room at different times. Methods. A generative model was applied to a patient’s preoperative anxiety aid diagnostic model and used to address preoperative anxiety due to the patient. Group ICA first constructed a whole-brain dynamic functional connectivity network, and an initial dataset was constructed by Kendall’s ranking correlation coefficient method; then, an expanded dataset was constructed by a conditional deep convolutional generative adversarial network (CDCGAN), and finally, a dynamic classification study of patients with preoperative anxiety and healthy subjects was conducted by feature selection and discriminant models in turn. Seventy patients who entered the operating theatre at different times were retrospectively analyzed. Children who entered the operating theatre after 14:00 were included in the control group (n=35), and those who entered between 12:00 were included in the observation group (n=35). Patients in both groups were given comprehensive care, and preoperative anxiety was compared between the two groups. Results. The earlier the patients entered the operating room, the lower the preoperative anxiety. Conclusion. Comprehensive care could reduce the patients’ preoperative anxiety, keep their emotions stable, and improve their compliance with treatment.


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## Body

## 1. Introduction

The admission, surgery, and discharge of patients within one working day is known as the day surgery model, which was first introduced in 1909 by the British pediatric surgeon Nichol [1]. The first independent day surgery center was established in the USA in 1970 [2]. In developed countries, the number of day surgery procedures has grown rapidly as surgical and anesthetic techniques have improved and patient acceptance has increased. In the UK, the proportion of elective surgery performed by day surgery rose from 15% to 70% between 1989 and 2003; in the USA, it rose from 35% to 83.5% between 1985 and 2003; over 1000 types of surgery are performed, and this proportion is increasing every year [3].The benefits of outpatient surgery are generally recognized and include the following: (1) hospitals: there are reduced length of stay, increased bed turnover and utilization, and increased access to care for patients; (2) patient side: it can shorten the waiting time for surgery, reduce the number of hospital days, reduce the financial burden, and reduce hospital-acquired infections; (3) economic aspects: it can reduce hospitalization time and avoid hospital-acquired infections largely reducing the cost of treatment for patients and alleviating the economic burden; (4) patients can return to normal life as soon as possible after surgery, reduce the mental burden caused by hospitalization and environmental changes, and are conducive to recuperation and rehabilitation; (5) caregiver load: it can reduce the psychological burden, time, and economic cost of family members caring for patients [4].Ambulatory surgery models have been studied in various areas, including anesthetic practice, nursing practice, patient experience, treatment outcomes, and economic benefits [5]. Numerous studies have shown that patients undergoing day surgery may face more significant psychological stress than those undergoing inpatient surgery and require effective professional support to help them cope with the day surgery process [6].A review of the literature found psychological recovery. Although surgery is a treatment for the disease, it is also a serious source of stress [2]. Increased bowel sounds usually manifest preoperative physiological stress; anxiety is the most typical clinical manifestation of preoperative psychological stress. Anxiety is a complex emotional response that includes tension. The anxiety level of patients before surgery tends to increase steadily, rising sharply to a peak just before anesthesia [7]. Moderate preoperative apprehension is beneficial to postoperative recovery [8]. A low level of anxiety indicates that the patient has a poor preoperative psychological state; is too dependent on medical staff or surgery; lacks sufficient psychological preparation for surgical risks, postoperative complications, and postoperative recovery; and may experience excessive anxiety due to postoperative pain. Excessive anxiety can lead to increased secretion of catecholamines and adrenocorticotropic hormones, especially glucocorticoids, which can delay wound healing [9]. There is a linear correlation between preoperative anxiety and various indicators of postoperative psychosomatic recovery (postoperative pain, postoperative satisfaction, time to exhaustion, etc. [10]). Preoperative anxiety has also been reported to increase the incidence of postoperative nausea and vomiting, aggravate postoperative pain, and increase the use of postoperative analgesics, which lead to cognitive impairment and prolong patient hospitalization. Postoperatively stressed patients have significantly increased rates of complication and mortality. The [11] study noted that patients with high anxiety levels should be given preoperative precautions. In summary, preoperative anxiety should be managed reasonably effectively to facilitate the physical and psychological recovery of patients after surgery.Coping is an individual’s cognitive and behavioral effort to deal with a stressful situation. On the other hand, medical coping is a patient’s conscious psychological strategy and behavioral response to illness [12]. In terms of whether coping helps to buffer the effects of stress and thus has a beneficial or detrimental effect on health, there are positive coping and negative coping [13], and studies have found that people who are not good at positive coping are more likely to experience anxiety. Wang et al. also showed that positive coping patients had less preoperative anxiety and negative coping patients had higher anxiety. It is generally accepted that positive coping is conducive to mobilizing the patient’s internal factors, enabling the patient to better cooperate with treatment and facilitating the stabilization and recovery of the disease, whereas negative coping enhances the patient’s stress response and reduces the body’s defense function against the disease, which has a detrimental effect on disease regression. Therefore, nursing staff should guide patients to adopt positive coping and avoid negative coping according to their personality characteristics to improve their psychological adaptability to surgical stress [14].Ambulatory surgery is a new surgical treatment modality, and patients’ unfamiliarity with the procedure, concerns about the safety of the procedure, concerns about inadequate self-care, and difficulties in adapting quickly to an unfamiliar surgical environment may increase their preoperative anxiety [15]. A phenomenological study of 20 UK adult day surgery patients showed that patients complained of feelings of abandonment while waiting for surgery before surgery [16]. A study of 112 children undergoing surgery showed that families of children undergoing interval surgery had significantly higher levels of preoperative anxiety than families of children undergoing inpatient surgery. All [17] studies showed that interval surgery patients had significant preoperative anxiety and a worrying level of anxiety. The [18] study noted that the majority of day surgery patients felt that preoperative information was important [19]. The study further noted that children undergoing interval surgery require intervention by professionals, including nurses.Health education and psychological interventions are effective and widely accepted nursing intervention methods. Oversea studies have shown that nursing interventions can effectively reduce preoperative psychological stress levels in day surgery patients. They play an important and positive role in reducing anxiety, decreasing nausea and vomiting, reducing the incidence of postoperative infections, and improving economic efficiency [20]. A preoperative intervention with a child life consultant in 142 children undergoing day surgery significantly reduced preoperative anxiety and increased child satisfaction. In [21], a music intervention was effective in reducing preoperative anxiety in patients undergoing interval surgery [22]. Music is effective as a preoperative anxiety management tool for patients undergoing interoperative surgery and is recommended as an effective preoperative anxiety management tool for patients undergoing day surgery [23]. A telephone follow-up of 238 patients undergoing interoperative procedures showed that many individualized problems could be easily addressed through adequate preoperative preparation and education.At present, the machine learning algorithms applied to preoperative patient anxiety are mainly traditional machine learning, such as SVM, random forests, K-NN, and shallow artificial neural networks. The basic principle of using machine learning to build patient preoperative anxiety prediction models is to collect data on patient preoperative anxiety risk factors and biomarkers and then preprocess these data to obtain a normalised dataset.


## 2. Methods

### 2.1. Patient Preoperative Anxiety Study Data Collection

The strength of machine learning lies in underlying mining patterns in data. Therefore, collecting data is the first step in building a predictive model. Data collection methods are becoming more diverse due to the development of science and technology, for example, questionnaire-based surveys, health data collected through various sensors, and publicly available data from online platforms. There is also a diversity of data collection methods and data types for machine learning-based studies of patient preoperative anxiety.The prediction of patient anxiety by machine learning is based on the factors that can reflect the patient’s preoperative anxiety tendency and various disease markers, including factors influencing the patient’s preoperative anxiety, symptoms, and physiological characteristics [24]. At present, there are mainly sociodemographic data such as age, gender and substance abuse, clinical data on physical symptoms and psychological status, and physiological data such as electroencephalography (EEG), magnetic resonance imaging (MRI), eye movement data, and heart rate variability parameters collected by medical instruments. EEG and MRI are the most widely used, but the cost of MRI and other physiological signals is high, so scholars are gradually exploring the possibility of collecting low-cost data that can predict patients’ preoperative anxiety [25, 26].


### 2.2. MVPA Process

The research framework in this section is constructed based on the MVPA framework, which mainly consists of feature extraction, feature selection, and classifier selection for the MVPA method. The CDCGAN applied in this section needs to make the number of samples as large as possible (i.e., the number of sliding windows constructed is large). Based on the experimental results of dynamic functionally connected networks in Section3, a sliding time window of (window width w=30, step size s=5) is used to construct the dynamic network and extract features, resulting in a final dataset of 41×23×741 (subjects×window×features). The initial dataset with a feature dimension of 100 was constructed using the Kendall ranking correlation coefficient method (the reason for constructing the dataset using the feature selection method is that reducing the dimensionality of the data through feature selection gives better results than dimensionality reduction). In addition, this section applies a hybrid feature selection algorithm for feature selection and distinguishes between the LDC classifier and LSVM classifier to train the discriminant model as shown in Figure 1.Figure 1
The classification procedure of depression based on CDCGAN.


#### 2.2.1. Building Expanded Datasets

We expanded the data by CDCGAN. Firstly, we used the Kendall ranking correlation coefficient method to sort the41×23×741 dataset by features and selected the top 100 dimensional features with strong discriminative power as the initial dataset (41×23×100), so as to remove redundant features and thus speed up the computation. Then, CDCGAN was used to generate samples with the same distribution as the initial data (the number of generated samples was the same as the number of samples in the initial data). Finally, the generated dataset and the initial dataset of the same subject are concatenated to form the (41×46×100) expanded dataset.CDCGAN is a network that incorporates the advantages of both classical generative adversarial networks DCGAN and CGAN, as shown in Figure2, consisting of the network G of DCGAN with the addition of constraint y. It is relatively easy to train, the model is controllable, and the generated samples are more in line with the researchers’ expectations. CDCGAN and DCGAN have a similar structure, with the generating network D and the discriminating network G being both restructured CNNs, where the discriminating network G is the core network of the generating network and is responsible for generating homogeneous data.Figure 2
The structure diagram of CDCGAN.The networkG in this study consists of an input layer (z and y), a fully connected layer, two convolutional layers, and an output layer and is shown schematically in Figure 3. In addition, the CDCGAN optimisation process is similar to that of other generative adversarial networks, in that it is a “binary minimal-extreme game” problem, in which the opponent’s error is maximized through alternate iterative updates (in this study, G is updated once for every two updates of D), and a Nash equilibrium is eventually reached between the two parties, as expressed in
(1)minGmaxDVD,G=Ex~pdataxlogDx,y+Ez~pzzlog1−DGz,y.Figure 3
The structure diagram of generator network.The detailed building blocks of the CDCGAN used in this study are as follows: (1) the network provided by carpedm20 is used for the generative networkG and the discriminative network D; (2) y is the ID number of the subject (1,2,⋯,41), and all windows of the same subject have the same ID number; (3) the noise z is a 100-dimensional random number obeying a standard Gaussian distribution; and (4) x is the initial dataset.


### 2.3. Case Studies

A retrospective analysis of 70 patients in our hospital was carried out preoperatively. They were divided into the control and observation groups according to the time of admission to the operating theatre, with 35 cases each.Before care, the difference in anxiety scores is shown in Table1.Table 1
Comparison of anxiety before and after care (x¯±s, %).
ProjectControl group (n=35)Observation group (n=35)Before nursing56.43±3.6456.43±3.64After nursing57.83±3.2157.83±3.21In the control group, 10 patients were positive, 19 were average, and 6 were negative, with a compliance rate of 82.86%; in the observation group, 11 patients were positive, 21 were average, and 3 were negative, with a compliance rate of 91.43%. There was no statistically significant difference in the compliance rate between the two groups [27].


## 2.1. Patient Preoperative Anxiety Study Data Collection

The strength of machine learning lies in underlying mining patterns in data. Therefore, collecting data is the first step in building a predictive model. Data collection methods are becoming more diverse due to the development of science and technology, for example, questionnaire-based surveys, health data collected through various sensors, and publicly available data from online platforms. There is also a diversity of data collection methods and data types for machine learning-based studies of patient preoperative anxiety.The prediction of patient anxiety by machine learning is based on the factors that can reflect the patient’s preoperative anxiety tendency and various disease markers, including factors influencing the patient’s preoperative anxiety, symptoms, and physiological characteristics [24]. At present, there are mainly sociodemographic data such as age, gender and substance abuse, clinical data on physical symptoms and psychological status, and physiological data such as electroencephalography (EEG), magnetic resonance imaging (MRI), eye movement data, and heart rate variability parameters collected by medical instruments. EEG and MRI are the most widely used, but the cost of MRI and other physiological signals is high, so scholars are gradually exploring the possibility of collecting low-cost data that can predict patients’ preoperative anxiety [25, 26].


## 2.2. MVPA Process

The research framework in this section is constructed based on the MVPA framework, which mainly consists of feature extraction, feature selection, and classifier selection for the MVPA method. The CDCGAN applied in this section needs to make the number of samples as large as possible (i.e., the number of sliding windows constructed is large). Based on the experimental results of dynamic functionally connected networks in Section3, a sliding time window of (window width w=30, step size s=5) is used to construct the dynamic network and extract features, resulting in a final dataset of 41×23×741 (subjects×window×features). The initial dataset with a feature dimension of 100 was constructed using the Kendall ranking correlation coefficient method (the reason for constructing the dataset using the feature selection method is that reducing the dimensionality of the data through feature selection gives better results than dimensionality reduction). In addition, this section applies a hybrid feature selection algorithm for feature selection and distinguishes between the LDC classifier and LSVM classifier to train the discriminant model as shown in Figure 1.Figure 1
The classification procedure of depression based on CDCGAN.


### 2.2.1. Building Expanded Datasets

We expanded the data by CDCGAN. Firstly, we used the Kendall ranking correlation coefficient method to sort the41×23×741 dataset by features and selected the top 100 dimensional features with strong discriminative power as the initial dataset (41×23×100), so as to remove redundant features and thus speed up the computation. Then, CDCGAN was used to generate samples with the same distribution as the initial data (the number of generated samples was the same as the number of samples in the initial data). Finally, the generated dataset and the initial dataset of the same subject are concatenated to form the (41×46×100) expanded dataset.CDCGAN is a network that incorporates the advantages of both classical generative adversarial networks DCGAN and CGAN, as shown in Figure2, consisting of the network G of DCGAN with the addition of constraint y. It is relatively easy to train, the model is controllable, and the generated samples are more in line with the researchers’ expectations. CDCGAN and DCGAN have a similar structure, with the generating network D and the discriminating network G being both restructured CNNs, where the discriminating network G is the core network of the generating network and is responsible for generating homogeneous data.Figure 2
The structure diagram of CDCGAN.The networkG in this study consists of an input layer (z and y), a fully connected layer, two convolutional layers, and an output layer and is shown schematically in Figure 3. In addition, the CDCGAN optimisation process is similar to that of other generative adversarial networks, in that it is a “binary minimal-extreme game” problem, in which the opponent’s error is maximized through alternate iterative updates (in this study, G is updated once for every two updates of D), and a Nash equilibrium is eventually reached between the two parties, as expressed in
(1)minGmaxDVD,G=Ex~pdataxlogDx,y+Ez~pzzlog1−DGz,y.Figure 3
The structure diagram of generator network.The detailed building blocks of the CDCGAN used in this study are as follows: (1) the network provided by carpedm20 is used for the generative networkG and the discriminative network D; (2) y is the ID number of the subject (1,2,⋯,41), and all windows of the same subject have the same ID number; (3) the noise z is a 100-dimensional random number obeying a standard Gaussian distribution; and (4) x is the initial dataset.


## 2.2.1. Building Expanded Datasets

We expanded the data by CDCGAN. Firstly, we used the Kendall ranking correlation coefficient method to sort the41×23×741 dataset by features and selected the top 100 dimensional features with strong discriminative power as the initial dataset (41×23×100), so as to remove redundant features and thus speed up the computation. Then, CDCGAN was used to generate samples with the same distribution as the initial data (the number of generated samples was the same as the number of samples in the initial data). Finally, the generated dataset and the initial dataset of the same subject are concatenated to form the (41×46×100) expanded dataset.CDCGAN is a network that incorporates the advantages of both classical generative adversarial networks DCGAN and CGAN, as shown in Figure2, consisting of the network G of DCGAN with the addition of constraint y. It is relatively easy to train, the model is controllable, and the generated samples are more in line with the researchers’ expectations. CDCGAN and DCGAN have a similar structure, with the generating network D and the discriminating network G being both restructured CNNs, where the discriminating network G is the core network of the generating network and is responsible for generating homogeneous data.Figure 2
The structure diagram of CDCGAN.The networkG in this study consists of an input layer (z and y), a fully connected layer, two convolutional layers, and an output layer and is shown schematically in Figure 3. In addition, the CDCGAN optimisation process is similar to that of other generative adversarial networks, in that it is a “binary minimal-extreme game” problem, in which the opponent’s error is maximized through alternate iterative updates (in this study, G is updated once for every two updates of D), and a Nash equilibrium is eventually reached between the two parties, as expressed in
(1)minGmaxDVD,G=Ex~pdataxlogDx,y+Ez~pzzlog1−DGz,y.Figure 3
The structure diagram of generator network.The detailed building blocks of the CDCGAN used in this study are as follows: (1) the network provided by carpedm20 is used for the generative networkG and the discriminative network D; (2) y is the ID number of the subject (1,2,⋯,41), and all windows of the same subject have the same ID number; (3) the noise z is a 100-dimensional random number obeying a standard Gaussian distribution; and (4) x is the initial dataset.


## 2.3. Case Studies

A retrospective analysis of 70 patients in our hospital was carried out preoperatively. They were divided into the control and observation groups according to the time of admission to the operating theatre, with 35 cases each.Before care, the difference in anxiety scores is shown in Table1.Table 1
Comparison of anxiety before and after care (x¯±s, %).
ProjectControl group (n=35)Observation group (n=35)Before nursing56.43±3.6456.43±3.64After nursing57.83±3.2157.83±3.21In the control group, 10 patients were positive, 19 were average, and 6 were negative, with a compliance rate of 82.86%; in the observation group, 11 patients were positive, 21 were average, and 3 were negative, with a compliance rate of 91.43%. There was no statistically significant difference in the compliance rate between the two groups [27].


## 3. Results

### 3.1. Classification Results

Applying the hybrid feature selection method, the best correct classification rates using the combined T-LSVM and T-LDC methods were 0.6585 and 0.6829, respectively. These two combined methods, which did not use CDCGAN, served as the baseline control for the experiment.A hybrid feature selection method was used for feature selection for the expanded dataset, and the LSVM and LDC classifier were applied for classification, respectively. For the leave-one-out cross-validation method, the training set was the expanded dataset of 40 subjects, and the test set was the initial dataset and the expanded dataset of the remaining 1 subject. The best classification accuracy of 92.68% for both test sets was achieved using different classifiers. As the parameters of CDCGAN (noisez and network weight w) are initialized randomly, which may affect the quality of the generated data, 10 rounds of CDCGAN experiments were conducted. Each experiment iteration was conducted 1000 times, and the generation samples were extracted once every 50 iterations. 20 sets of generation samples were extracted, which resulted in an expanded dataset of 10×20 sets. Figure 4 shows four expanded datasets’ optimal classification results under different methods.Figure 4
The classification results with CDCDAN and without CDCDAN.As the CDCGAN in the experiments had randomly initialized parameters, in order to evaluate the performance of the CDCGAN using an aided diagnostic model, the overall10×20 expanded datasets were summarised. Figure 5 shows the average growth rate (mean±variance) of the best correct classification rate for the expanded dataset using the four combinations of methods relative to the baseline control best classification rate when the parameter N is varied.Figure 5
The average growth rate of the best accuracy rate for the parameterN.As seen from Figure5, the average growth rate of the best correct classification rate for all four combinations of methods lies above the zero scale line, indicating that the CDCGAN method helps to improve the correct classification rate. To further analyze the effect of different feature combinations on classification, Figure 6 gives the average growth rate of the correct classification rate for different feature dimensions for N=85.Figure 6
The average growth rate of the classification accuracy under different feature dimensions.


### 3.2. Analysis of Results

The effects of the CDCGAN generation model, the parameterN of the hybrid feature selection algorithm, and the combination of features on the classification of patient preoperative anxiety based on fMRI data are presented in Figures 4, 5, and 6, respectively. These three figures not only show that the application of CDCGAN can significantly improve the correct classification rate of patient preoperative anxiety but also demonstrate the feasibility of applying CDCGAN to preoperative patient anxiety MRI data. In order to further analyze the impact of the model proposed in this chapter on the feature contribution, a specific analysis of the dataset (c) in Figure 4 was carried out. The classification process uses the leave-one-out cross-validation method for feature selection and classification for each subject, which may result in a different combination of features filtered out by feature selection each time due to the small differences in the training set in each cross-validation. Using the dataset (c) in Figure 4, we compared the best correct classification rates using different feature selection methods and different combinations of methods, as shown in Table 2.Table 2
The best classification results and corresponding parameter values.
Combination methodKendallHybrid feature selection algorithmCorrect rateCharacteristic numberCorrect rateParameterNCharacteristic numberM-LSVM0.9024490.92688140M-LDC0.9024450.90246543O-LSVM0.7561480.85376533O-LDC0.8469470.87806946From Table2, it can be seen that for the same training and test sets, the hybrid feature selection algorithm achieves higher classification accuracy, and it can also be seen that the classification accuracy achieved by applying the CDCGAN model, regardless of the feature selection method, is higher than that achieved without the CDCGAN model.


## 3.1. Classification Results

Applying the hybrid feature selection method, the best correct classification rates using the combined T-LSVM and T-LDC methods were 0.6585 and 0.6829, respectively. These two combined methods, which did not use CDCGAN, served as the baseline control for the experiment.A hybrid feature selection method was used for feature selection for the expanded dataset, and the LSVM and LDC classifier were applied for classification, respectively. For the leave-one-out cross-validation method, the training set was the expanded dataset of 40 subjects, and the test set was the initial dataset and the expanded dataset of the remaining 1 subject. The best classification accuracy of 92.68% for both test sets was achieved using different classifiers. As the parameters of CDCGAN (noisez and network weight w) are initialized randomly, which may affect the quality of the generated data, 10 rounds of CDCGAN experiments were conducted. Each experiment iteration was conducted 1000 times, and the generation samples were extracted once every 50 iterations. 20 sets of generation samples were extracted, which resulted in an expanded dataset of 10×20 sets. Figure 4 shows four expanded datasets’ optimal classification results under different methods.Figure 4
The classification results with CDCDAN and without CDCDAN.As the CDCGAN in the experiments had randomly initialized parameters, in order to evaluate the performance of the CDCGAN using an aided diagnostic model, the overall10×20 expanded datasets were summarised. Figure 5 shows the average growth rate (mean±variance) of the best correct classification rate for the expanded dataset using the four combinations of methods relative to the baseline control best classification rate when the parameter N is varied.Figure 5
The average growth rate of the best accuracy rate for the parameterN.As seen from Figure5, the average growth rate of the best correct classification rate for all four combinations of methods lies above the zero scale line, indicating that the CDCGAN method helps to improve the correct classification rate. To further analyze the effect of different feature combinations on classification, Figure 6 gives the average growth rate of the correct classification rate for different feature dimensions for N=85.Figure 6
The average growth rate of the classification accuracy under different feature dimensions.


## 3.2. Analysis of Results

The effects of the CDCGAN generation model, the parameterN of the hybrid feature selection algorithm, and the combination of features on the classification of patient preoperative anxiety based on fMRI data are presented in Figures 4, 5, and 6, respectively. These three figures not only show that the application of CDCGAN can significantly improve the correct classification rate of patient preoperative anxiety but also demonstrate the feasibility of applying CDCGAN to preoperative patient anxiety MRI data. In order to further analyze the impact of the model proposed in this chapter on the feature contribution, a specific analysis of the dataset (c) in Figure 4 was carried out. The classification process uses the leave-one-out cross-validation method for feature selection and classification for each subject, which may result in a different combination of features filtered out by feature selection each time due to the small differences in the training set in each cross-validation. Using the dataset (c) in Figure 4, we compared the best correct classification rates using different feature selection methods and different combinations of methods, as shown in Table 2.Table 2
The best classification results and corresponding parameter values.
Combination methodKendallHybrid feature selection algorithmCorrect rateCharacteristic numberCorrect rateParameterNCharacteristic numberM-LSVM0.9024490.92688140M-LDC0.9024450.90246543O-LSVM0.7561480.85376533O-LDC0.8469470.87806946From Table2, it can be seen that for the same training and test sets, the hybrid feature selection algorithm achieves higher classification accuracy, and it can also be seen that the classification accuracy achieved by applying the CDCGAN model, regardless of the feature selection method, is higher than that achieved without the CDCGAN model.


## 4. Discussion

During the preoperative waiting time in the operating room, patients are prone to bad emotions that affect the outcome of the operation. Patients often think about the risks, complications, trauma, and outcomes of surgery, which increases their anxiety about the procedure. Performing surgery before 12:00 noon can shorten the time to worry about the risks of surgery, reduce preoperative anxiety, and ensure that patients have enough energy for surgery. The longer you wait for surgery, the more anxious the patient becomes, and the more physically and mentally exhausted he or she becomes. Therefore, in addition to shortening the waiting time for surgery, effective care is also given to patients during the waiting period. Psychological, environmental, and dietary care is given to patients to improve their physiological and psychological conditions and to avoid mood swings during the waiting period. Psychological care can relieve patients’ anxiety during the waiting period and improve their confidence in the treatment; environmental care can eliminate patients’ unfamiliarity with the treatment environment and reduce mood swings; dietary care can improve patients’ immune system and ensure that they have sufficient physical strength and energy to complete the operation, thus improving the safety of the operation.This study also has some shortcomings. First, the patients in this study were all from the same hospital, which is not representative of the overall situation. Secondly, the sample size of this study is too small, which can easily lead to biased results. Finally, this study only compared the comprehensive nursing effect before and after operation, but whether there are differences in nursing effect at different time points after operation, one point still needs further study.This paper is the first to apply a generative model (CDCGAN) to a patient’s preoperative anxiety-assisted diagnostic model and to address the problem that some machine learning algorithms cannot be used due to the insufficient number of available samples of patient preoperative anxiety fMRI data. Group ICA constructed a whole-brain dynamic functional connectivity network, and the initial dataset was constructed by Kendall’s ranking correlation coefficient method. The results showed that the correct classification rate based on CDCGAN was significantly higher than that without CDCGAN.


## 5. Conclusions

This indicates that the MVPA-assisted diagnosis model for patient preoperative anxiety based on fMRI data using the hybrid feature selection algorithm and CDCGAN proposed in this paper is a valid and feasible attempt.



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*Source: 2154599-2022-07-27.xml*