Fuzzy Decision Tree Based Method in Decision-Making of COVID-19 Patients’ Treatment

A new method in decision-making of timing of tracheostomy in COVID-19 patients is developed and discussed in this paper. Tracheostomy is performed in critically ill coronavirus disease (COVID-19) patients. The timing of tracheostomy is important for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. The analysis of this timing has been implemented based on classification method. One of principal conditions for the developed classifiers in decision-making of timing of tracheostomy in COVID-19 patients was a good interpretation of result. Therefore, the proposed classifiers have been developed as decision tree based because these classifiers have very good interpretability of result. The possible uncertainty of initial data has been considered by the application of fuzzy classifiers. Two fuzzy classifiers as Fuzzy Decision Tree (FDT) and Fuzzy Random Forest (FRF) have been developed for the decision-making in tracheostomy timing. The evaluation of proposed classifiers and their comparison with other show the efficiency of the proposed classifiers. FDT has best characteristics in comparison with other classifiers.

Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation

To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers.

Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.

Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small.