In today’s world, heart disease is the leading cause of death globally. Researchers have proposed various methods aimed at improving the accuracy and efficiency of the clinical diagnosis of heart disease. Auxiliary diagnostic systems based on machine learning are designed to learn and predict the disease status of patients from a large amount of pathological data. Practice has proved that such a system has the potential to save more lives. Therefore, this paper proposes a new framework for predicting heart disease using the smote-xgboost algorithm. First, we propose a feature selection method based on information gain, which aims to extract key features from the dataset and prevent model overfitting. Second, we use the Smote-Enn algorithm to process unbalanced data, and obtain sample data with roughly the same positive and negative categories. Finally, we test the prediction effect of Xgboost algorithm and five other baseline algorithms on sample data. The results show that our proposed method achieves the best performance in the five indicators of accuracy, precision, recall, F1-score and AUC, and the framework proposed in this paper has significant advantages in heart disease prediction.

In recent years, data has become a special kind of information commodity and promoted the development of information commodity economy through distribution. With the development of big data, the data market emerged and provided convenience for data transactions. However, the issues of optimal pricing and data quality allocation in the big data market have not been fully studied yet. In this paper, we proposed a big data market pricing model based on data quality. We first analyzed the dimensional indicators that affect data quality, and a linear evaluation model was established. Then, from the perspective of data science, we analyzed the impact of quality level on big data analysis (i.e., machine learning algorithms) and defined the utility function of data quality. The experimental results in real data sets have shown the applicability of the proposed quality utility function. In addition, we formulated the profit maximization problem and gave theoretical analysis. Finally, the data market can maximize profits through the proposed model illustrated with numerical examples.

Background: According to a recent report by the WHO, approximately 17.5\% (about one-sixth) of the global adult population is affected by infertility. Consequently, researchers worldwide have proposed various machine learning models to improve the prediction of clinical pregnancy outcomes during IVF cycles. The objective of this study is to develop a machine learning(ML) model that predicts the outcomes of pregnancies following in vitro fertilization (IVF) and assists in clinical treatment.Methods: This study conducted a retrospective analysis on provincial reproductive centers in China from March 2020 to March 2021, utilizing 13 selected features. The algorithms used included XGBoost, LightGBM, KNN, Naïve Bayes, Random Forest, and Decision Tree. The results were evaluated using performance metrics such as precision, recall, F1-score, accuracy and AUC, employing five-fold cross-validation repeated five times.Results: Among the models, LightGBM achieved the best performance, with an accuracy of 92.31%, recall of 87.80%, F1-score of 90.00\%, and an AUC of 90.41%. The model identified the estrogen concentration at the HCG injection(etwo), endometrium thickness (mm) on HCG day(EM TNK), years of infertility(Years), and body mass index(BMI) as the most important features.Conclusion: This study successfully demonstrates the LightGBM model has the best predictive effect on pregnancy outcomes during IVF cycles. Additionally, etwo was found to be the most significant predictor for successful IVF compared to other variables. This machine learning approach has the potential to assist fertility specialists in providing counseling and adjusting treatment strategies for patients.

In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due to privacy issues, access to such data is limited. Given the business opportunities that have gaps between demand and supply, we consider establishing a private data market to resolve supply and demand conflicts. While there are many challenges to building such a data market, we only focus on three technical challenges: (1) How to provide a fair trading mechanism between data providers and data platforms? (2) What is the consumer’s attitude toward privacy data? (3) How to price personal data to maximize the profit of the data platform? In this paper, we first propose a compensation mechanism based on the privacy attitude of the data provider. Second, we analyze consumer self-selection behavior and establish a non-linear model to represent consumers’ willingness to pay (WTP). Finally, we establish a bi-level programming model and use genetic simulated annealing algorithm to solve the optimal pricing problem of personal data. The experimental results show that multi-level privacy division can meet the needs of consumers and maximize the profit of data platform.

In the era of big data, medical data sharing has become an inevitable requirement to improve the quality and efficiency of medical services. To advance the progress of medical data sharing and expedite the circulation of data value, it becomes crucial to examine the decision-making behavior of stakeholders involved in the medical-data-sharing process. To this end, we construct a three-way evolutionary game model applicable to the medical sharing scenario, analyzing the evolutionary trends in the selection strategies of data providers, the medical-data-sharing platform and data demanders. Furthermore, through theoretical analysis and simulation experiments, we explore the game equilibrium point of the system and analyze key factors that affect stakeholder strategy selection. The results of the experiment show that, in addition to data security, platforms and regulators should pay attention to the regulation and governance of the quality of data flows, which involves reasonable incentives–feedback–rewards and penalties. By strengthening the security technology and data governance system construction of sharing platforms, as well as promoting regulatory authorities to implement reward and punishment measures, etc., a stable state can be achieved in such systems. In addition, this article also proposes relevant management suggestions for medical data sharing in order to provide useful references for scientific decision making by stakeholders.

With the rapid development of information technology, online knowledge monetization has emerged as a potential business model for several social Q&A platforms, which brings more complex benefits and cost tradeoffs for users. However, most of the previous studies were based on free Q&A platforms, which made it difficult to explain the influencing factors of various stakeholders in the decision‐making process of knowledge sharing and failed to comprehensively analyze the behavior of knowledge sharing subjects using dynamic system theory. This study establishes a tripartite evolutionary game model of knowledge consumers, knowledge providers, and knowledge payment platforms and discusses evolutionary stability strategies, evolutionary trends, and factors affecting the evolutionary path of tripartite behavior. The extensive experimental results show that knowledge payment is an important way to promote knowledge sharing. Meanwhile, the implementation of a rewards and punishment system on payment platforms will encourage knowledge providers to provide high‐quality knowledge content and increase the willingness of knowledge consumers to pay, thereby facilitating the knowledge‐sharing process. This paper provides the decision‐making basis for the operation of the online knowledge community and proposes a new proposition to solve the conflict of interest in the process of knowledge sharing from the perspective of evolutionary theory.