Constructing a Financial Risk Early Warning Model for Chinese Public Hospitals Based on Machine Learning

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Background
Against the backdrop of China's evolving healthcare system and increasing demand for healthcare, public hospitals are responsible for a wide range of healthcare responsibilities as the primary healthcare providers.However, public hospitals are facing increasingly significant financial challenges, which are influenced by a variety of factors, including fluctuations in demand for healthcare services, adjustments in healthcare policies, and reforms in the healthcare insurance system.
Traditional financial management methods are often difficult to cope with these changing factors, and the financial situation of public hospitals has become more complex and uncertain.In the face of this situation, the establishment of an efficient financial risk early warning system has become an urgent need for public hospital management.This system not only needs to be sensitive to detect financial problems in a timely manner, but also needs to be predictive to gain insight into potential financial risks in advance.
Against this challenging background, machine learning algorithms have emerged to provide an innovative approach to financial management for public hospitals.By analyzing large-scale financial data, machine learning algorithms are able to identify potential patterns and trends, thus providing more accurate and intelligent support for financial decision-making.Therefore, the application of machine learning algorithms to construct financial risk early warning models for Chinese public hospitals has become a hotspot and focus of attention in current research.
The Nature of Hospital Financial Challenges: Public hospitals face multifaceted financial management challenges such as uneven medical services, changes in health insurance policies, and fluctuations in patients' willingness to pay.The interaction of these factors leads to a high degree of complexity in financial decision-making, requiring more intelligent and flexible management tools.
The rise of machine learning: In recent years, the successful application of machine learning in various fields has attracted widespread attention.In the medical field, machine learning algorithms have achieved remarkable results in disease diagnosis and patient prediction.In order to better meet the challenges of hospital financial management, researchers have begun to introduce this powerful tool into the field of financial risk early warning, with a view to constructing more intelligent and accurate models.
Motivation and significance of the study: Given the shortcomings of traditional financial management methods, this study aims to gain insight into the potential application of machine learning in financial risk early warning in public hospitals.By reviewing and analyzing the existing literature, we hope to gain a comprehensive understanding of the nature of financial risks faced by public hospitals, and to identify the advantages and challenges of machine learning algorithms in solving these problems.

Purpose and Application of Research
The purpose of this paper is to review and analyze past research on financial risk early warning in Chinese public hospitals, focusing on the practice and results of using machine learning algorithms to construct models.By synthesizing and analyzing the existing literature, we aim to provide public hospital management with more comprehensive financial information and make useful suggestions for future research and practice.
Machine learning is increasingly used in the medical field, including disease diagnosis, patient prediction, and drug development.In the field of financial management, the introduction of machine learning provides a new perspective for hospitals to improve the accuracy and efficiency of financial decision-making by analyzing complex financial data.

Domestic and International Research Status
In the modern healthcare system, the financial management of public hospitals has always been the focus of much attention.Dynamic changes in the demand for healthcare services, frequent adjustments in healthcare policies, and uncertainty in patients' ability to pay are among the factors that make public hospitals face multilayered financial challenges.Traditional financial management methods rely on retrospective analysis of historical data, which is gradually becoming inadequate in today's complex and fast-changing healthcare environment, and new intelligent means are needed to better adapt to the changing healthcare service needs and financial environment.
In recent years, the wide application of machine learning in the field of financial management has aroused great interest among researchers.By analyzing large-scale financial data, machine learning algorithms are able to identify patterns and trends hidden behind the data and improve sensitivity and accuracy to financial risks.In other industrial fields, such as finance and manufacturing, machine learning has been successfully applied to optimize decision-making, reduce risk and improve efficiency, which provides a strong theoretical basis for its application in the financial management of public hospitals.The successful application of machine learning algorithms in the healthcare sector provides ample support for their potential value in the financial management of public hospitals.In the medical field, machine learning has been widely used in disease prediction, patient classification, and drug development.These success stories show that machine learning has a promising future in processing medical data and improving the efficiency of medical services.
Established studies have achieved some results in financial risk early warning in public hospitals, with some focusing on constructing traditional financial models, while others have begun to explore the possibility of introducing machine learning algorithms.However, existing studies generally suffer from insufficient sample size, insufficiently systematic feature selection, and weak model interpretability.These limitations restrict the reliability and operability of the existing studies in practical applications.Despite the remarkable achievements of machine learning in the healthcare field, its application to financial risk early warning models in public hospitals still faces a series of challenges.These include the problem of feature selection, i.e., how to select important features related to financial risks from a large amount of data; data quality assurance, to ensure that the data input to the model is accurate, complete and reliable; and model interpretability, so that hospital managers can understand the decision-making process of the model.

Prospect
Future research can further deepen the application of machine learning algorithms in financial risk early warning in public hospitals.More advanced and applicable machine learning algorithms, such as deep learning and reinforcement learning, can be explored for the financial characteristics of hospitals in order to improve the accuracy and real-time financial prediction.Meanwhile, the integration of multi-source data, including financial data, patient consultation records, and health insurance data, can be considered in order to establish a more comprehensive and multi-dimensional financial risk early warning model.The integrated use of multi-source data will help capture potential financial risk signals more accurately and improve the comprehensiveness and reliability of the model.
Future research could move toward the development of actionable decision support tools that enable the model's predictions to provide practical advice directly to hospital management.This includes the design of user-friendly interfaces and the provision of real-time updated early warning information to help managers better formulate financial strategies.Customize the financial risk early warning model to take into account the size and geographical characteristics of different public hospitals.This will help the models to better adapt to the differences in different healthcare environments and improve the feasibility and effectiveness of their practical application.Considering the time-varying and dynamic nature of the healthcare service sector, future research can further enhance the real-time monitoring and feedback mechanism of the model.This means that the model should be able to flexibly adapt to new data and environmental changes, thus improving the adaptability and sustainability of the model.
Ultimately, the goal of future research should be to promote the integrated development of intelligent healthcare management, combining the financial risk early warning model with other healthcare information systems and hospital management platforms to form an efficient and intelligent healthcare service management system that provides comprehensive decision support for public hospitals.

Method
In this study, four machine learning algorithms, namely decision trees, support vector machines and random forests are used for the construction of early warning models.

Machine Learning
Decision Tree: A decision tree is a model for decision making based on a tree-graph structure, which is formed by dividing the data set into different subsets, each of which corresponds to a node of the tree, ultimately forming a tree structure.In a decision tree, each internal node represents a test for an attribute, each branch represents the result of the test, and each leaf node (also called a terminal node) stores a category label.A path from the root node to the leaf nodes allows new data to be categorized or predicted.Support Vector Machine: Support vector machine is a supervised learning algorithm for classification and regression.The main idea is to find an optimal hyperplane in the feature space that can separate samples of different classes and maximize the interval from the samples to the hyperplane.The key to a support vector machine is to find the support vectors, i.e., those sample points closest to the hyperplane that play a key role in determining the optimal hyperplane.Random Forest: Random Forest is an integrated learning method that performs classification and regression by combining multiple decision trees.It is a forest of multiple decision trees, each trained independently and averaged (for classification problems) or averaged (for regression problems) to improve the performance and robustness of the overall model.

Model Building Steps
Data Preparation: Collect financial data of the hospital, including total annual revenue, cost of medical services, number of patient visits, etc., as well as financial health status as the target variable.Model Evaluation: Evaluate the model using a test set to calculate metrics such as accuracy, recall, etc.

Results
In this study, we explore the performance of three different machine learning models, Decision Trees, Random Forests and Support Vector Machines (SVMs), on financial risk prediction.The following is a detailed analysis of these three models in terms of accuracy, precision, recall, F1 score and AUC: Decision tree modeling: Accuracy is 0.959, Precision (state 0 / state 1) is 0.967 / 0.500, Recall (state 0 / state 1) is 0.991 / 0.214, F1 score (state 0 / state 1) is 0.979 / 0.300, AUC is 0.603.The decision tree performs well in predicting a financial health state of 0, but has relatively low recall in state 1.The model may have some underreporting problems in the face of financial risk.See in Table 1 and Figure 1.Random Forest Modelingis Accuracyis 0.966, Precision (state 0 / state 1) is 0.966 / 1.000, Recall (state 0 / state 1) is 1.000 / 0.042, F1 score (state 0 / state 1) is 0.983 / 0.080, AUC is 0.871.Random Forest is relatively good in terms of overall performance, but still faces the challenge of low recall in state 1, which means that the model may have some degree of underreporting in the face of financial risk.See in Table 2 and Figure    Support vector machine modelingis Accuracyis 0.962, Precision (state 0 / state 1) is 0.963 / 0.000, Recall (state 0 / state 1) is 0.998 / 0.000, F1 score (state 0 / state 1) is 0.981 / NaN (due to zero denominator), AUC is 0.499.The predictive performance of the Support Vector Machine in state 1 is very limited, and a recall of zero indicates that the model fails to correctly identify financial risks.See in Table 3 and Figure 3.  From the accuracy point of view, the random forest model is slightly better than the other two models.At a financial health state of 0, the decision tree and random forest have relatively high recall, while the support vector machine is lower.In the case of a financial health status of 1, all models face the problem of low recall, which may result in financial risks not being adequately identified.The F1 score combines precision and recall, and Random Forest has a higher F1 score at state 0. In terms of AUC, the random forest model performs relatively well, while the AUC of the support vector machine is close to random classification.

Model Advantages and Disadvantages
Strengths and weaknesses of decision tree modeling: Strengths: Ease of Understanding and Interpretation: The decision rules of decision trees are intuitive and easy to understand, enabling lay people to understand and interpret how the model works.
Adapts to non-linear relationships: Decision trees are able to handle non-linear relationships effectively and are suitable for complex associations that may exist in healthcare data.
Insensitive to Missing Values: Decision trees are insensitive to missing values and are able to handle missing information present in the dataset.
Insufficient: Prone to overfitting: Decision trees are prone to overfitting when dealing with complex problems, especially when the depth of the tree is too large, which may lead to overfitting the training data and reduce the generalization performance on unseen data.
Sensitivity to data noise: Decision trees are sensitive to noise and outliers in the data, which may lead to unstable prediction results.
Instability: Small changes to the data may result in a completely different tree structure, making the model less stable.
Strengths and weaknesses of the Random Forest model: Strengths: High accuracy: Random forests are generally highly accurate and reduce the risk of overfitting individual trees by integrating multiple decision trees.
Assessment of Feature Importance: Random Forests can provide information on feature importance to help identify the most critical factors in financial risk prediction.
Processing of large-scale data: Random forests are able to effectively process large-scale data with a high degree of parallelism.
Shortcomings: Higher computational cost: Random forests may require more computational resources for training and prediction, especially when the number of trees is large.
Lack of Explanation: The model structure of Random Forest is relatively complex and poorly explained, making Support vector machines hold promise for a wide range of applications in financial risk prediction in healthcare with their ability to handle high dimensional and complex data.Their nonlinear modeling capabilities allow them to better capture the complex relationships present in healthcare data.However, support vector machines may be limited in practical applications due to their long training time for large-scale data and sensitivity to computational resources.Its interpretability is relatively poor, and the need for transparency and interpretability in medical decision-making scenarios needs to be considered comprehensively.
In the medical field, ethical issues and privacy protection are crucial.Decision trees are relatively easy to interpret and therefore better meet the requirements of transparency and interpretability in medical decision making.Random forests and support vector machines, on the other hand, need to deal with privacy and ethical issues more carefully, taking measures such as encryption and desensitization to ensure the privacy and security of patient data.Healthcare organizations need to consider factors such as model performance, computational efficiency, and ethical and privacy protection when choosing a model.

Basic
Financial Data: Total annual revenue, Cost of medical services, Cost-to-revenue ratio (cost of medical services/total annual revenue), Number of patient visits Financial Health Status Label: Flags if financial risk is present as a target variable.Patient Information: Number of patients, Number of visits/number of patients (average number of visits per patient), Patient age distribution Medical service data: Type of medical service, Average service cost Personnel costs and operational data: Number of medical staff, Cost per capita, Operating Costs Management and decision-making data: Management Level, Decision-making response time Geographic and demographic information: Economic status of the area where the hospital is located, Size of population served Previous Financial Data: Trends in financial data over the past few years Feature standardization: Standardize the features so that they have the same scale.Data preprocessing: Handle missing values, outliers, and perform feature engineering to select appropriate features.Data division: Divide the dataset into training and test sets.Model Construction: Select appropriate index parameters to construct the model.Model Training: Train the model using the training set.

Table 1 .
Decision tree evaluation metrics All metrics are calculated for every class against all other classes.

Table 2 .
Random forest evaluation metrics Note.All metrics are calculated for every class against all other classes.

Table 3 .
Support vector machine evaluation metrics All metrics are calculated for every class against all other classes.