decision tree algorithm
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In patients with severe forms of COVID-19, thromboelastometry has been reported to display a hypercoagulant pattern. However, an algorithm to differentiate severe COVID-19 patients from nonsevere patients and healthy controls based on thromboelastometry parameters has not been developed. Forty-one patients over 18 years of age with positive qRT-PCR for SARS-CoV-2 were classified according to the severity of the disease: nonsevere (NS, n = 20) or severe (S, n = 21). A healthy control (HC, n = 9) group was also examined. Blood samples from all participants were tested by extrinsic (EXTEM), intrinsic (INTEM), non-activated (NATEM) and functional assessment of fibrinogen (FIBTEM) assays of thromboelastometry. The thrombodynamic potential index (TPI) was also calculated. Severe COVID-19 patients exhibited a thromboelastometry profile with clear hypercoagulability, which was significantly different from the NS and HC groups. Nonsevere COVID-19 cases showed a trend to thrombotic pole. The NATEM test suggested that nonsevere and severe COVID-19 patients presented endogenous coagulation activation (reduced clotting time and clot formation time). TPI data were significantly different between the NS and S groups. The maximum clot firmness profile obtained by FIBTEM showed moderate/elevated accuracy to differentiate severe patients from NS and HC. A decision tree algorithm based on the FIBTEM-MCF profile was proposed to differentiate S from HC and NS. Thromboelastometric parameters are a useful tool to differentiate the coagulation profile of nonsevere and severe COVID-19 patients for therapeutic intervention purposes.

Finance, as the core of the modern economy, supports sustained economic growth through financing and distribution. With the continuous development of the market economy, finance plays an increasingly important role in economic development. A new economic and financial phenomenon, known as financial intervention, has emerged in recent years, which has created a series of new problems, promoting the rapid increase both in credit and investment and causing many problems on normal operation of financial bodies. In the long run, it will inevitably affect the stability and soundness of the entire economic and financial system. In order to maximize the effect of financial intervention, in response to the above problems, this article uses a series of US practices in financial intervention as the survey content, combined with the loan data provided by the US government financial intervention department, and mines the data of the general C4.5 algorithm of the decision tree algorithm. Generate a decision tree and convert it into classification rules. Next, we will discover the laws hidden behind the loan data, further discover information that may violate relevant financial policies, provide a reliable basis for financial intervention, and improve the efficiency of financial intervention. Experiments show that the method used in this article can effectively solve the above problems and has certain practicability in fiscal intervention. With stratified sampling, the risky accuracy rate increased by 10%, probably because stratified sampling increased the number of high-risk samples.

This paper requires a lot of assumptions for financial risk, which cannot use all of the data and is often limited to financial data; and in the past, most early warning models for financial crises did not, so they could not track the fluctuation and change trend of financial indicators. A decision tree algorithm model is used to propose a financial risk early warning method. Enterprises have suffered as a result of the financial crisis, and some have even gone bankrupt. Any financial crisis, on the other hand, has a gradual and deteriorating course. As a result, it is critical to track and monitor the company's financial operations so that early warning signs of a financial crisis can be identified and effective measures taken to mitigate the company’s business risk. This paper establishes a financial early warning system to predict financial operations using the decision tree algorithm in big data. Operators can take measures to improve their enterprise’s operation and prevent the failure of the embryonic stage of the financial crisis, to avoid greater losses after discovering the bud of the enterprise’s financial crisis, and to avoid greater losses after discovering the bud of the enterprise’s financial crisis. This prediction can be used by banks and other financial institutions to help them make loan decisions and keep track of their loans. Relevant businesses can use this signal to make credit decisions and effectively manage accounts receivable; CPAs can use this early warning information to determine their audit procedures, assess the enterprise's prospects, and reduce audit risk. As a result, the principle of steady operation should guide modern enterprise management. Prepare emergency plans in advance of a business risk or financial crisis to resolve the financial crisis and reduce the financial risk.

In order to improve the weight calculation accuracy of hospital informatization level evaluation and shorten the evaluation time, a research method of hospital informatization level evaluation model based on the decision tree algorithm is proposed. Using the decision tree algorithm combining fuzzy theory and ID3, the decision tree is constructed to analyze the hospital information data. By means of questionnaire survey, expert experience, mathematical statistics, and in-depth interview, information facilities construction, information resources construction, information scientific research application, management information, and information guarantee are selected as the nodes of the decision tree to evaluate the hospital information level. Construct the structural equation model, standardize the data, extract the weight of each evaluation index, and complete the evaluation of hospital informatization level. The experimental results show that the weight calculation results of this method are basically consistent with the actual results, and the evaluation efficiency is improved.

Abstract Global terror that has shaken the world named, COVID-19 virus has taken away huge number of lives. According to the research there are lot of recovery cases also. Most important thing to survive from this disease is having good immunity. Everyone does not have same level of immunity. One main factor on which immunity depends is having a healthy diet. If the routine of having healthy diet is maintained, then the immunity to fight against this virus increases. It is much required that people need to be informed about having an healthy diet. Using the dataset of healthy dietary and using various machine learning algorithms we can determine what type of diet one person needs to have. By using algorithms like Random Forest, KNN, logistic regression and Support Vector Machines we can determine the type of diet and probability of recovery. The dataset required for analysis needs to have all the information regarding the diet. Based on the dataset the prediction is taken place by using Decision Tree algorithm. This method of finding the appropriate diet of a particular person based on amount of Sugar level, Blood Pressure and BMI can be the most useful research in this pandemic time.

<abstract><p>To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid method by combining principal component analysis (PCA) and boosted C5.0 decision tree algorithm with penalty factor is proposed to address this issue. PCA is used to reduce the dimension of feature subset. The boosted C5.0 decision tree algorithm is utilized as an ensemble classifier for classification. Penalty factor is used to optimize the classification result. To demonstrate the efficiency of the proposed method, it is implemented on biased-representative breast cancer datasets from the University of California Irvine(UCI) machine learning repository. Given the experimental results and further analysis, our proposal is a promising method for breast cancer and can be used as an alternative method in class imbalance learning. Indeed, we observe that the feature extraction process has helped us improve diagnostic accuracy. We also demonstrate that the extracted features considering breast cancer issues are essential to high diagnostic accuracy.</p></abstract>