A less-greedy two-term Tsallis Entropy Information Metric approach for decision tree classification

An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome

Background

Acute respiratory distress syndrome (ARDS) is a clinical syndrome triggered by pulmonary or extra-pulmonary factors with high mortality and poor prognosis in the ICU. The aim of this study was to develop an interpretable machine learning predictive model to predict the risk of death in patients with ARDS in the ICU.

Methods

The datasets used in this study were obtained from two independent databases: Medical Information Mart for Intensive Care (MIMIC) IV and eICU Collaborative Research Database (eICU-CRD). This study used eight machine learning algorithms to construct predictive models. Recursive feature elimination with cross-validation is used to screen features, and cross-validation-based Bayesian optimization is used to filter the features used to find the optimal combination of hyperparameters for the model. The Shapley additive explanations (SHAP) method is used to explain the decision-making process of the model.

Results

A total of 5,732 patients with severe ADRS were included in this study for analysis, of which 1,171 patients (20.4%) did not survive. Among the eight models, XGBoost performed the best; AUC-ROC was 0.887 (95% CI: 0.863-0.909) and AUPRC was 0.731 (95% CI: 0.673-0.783).

Conclusion

We developed a machine learning-based model for predicting the risk of death of critically ill ARDS patients in the ICU, and our model can effectively identify high-risk ARDS patients at an early stage, thereby supporting clinical decision-making, facilitating early intervention, and improving patient prognosis.

Deconstruction of the Prevention of Knee Osteoarthritis by Swimming Based on Data Mining Technology

With the continuous development of big data and the continuous improvement of people's living standards, increasingly attention is paid to physical health. Swimming in this sport is effective in preventing the occurrence of arthritis. This paper analyzes the prevention and exploration of arthritis and relies on the traditional method of retrieving clinical literature on the treatment of knee osteoarthritis with traditional Chinese medicine and internal medicine, which requires a lot of manpower and material resources. At this time, the role of data mining technology is brought into play. This article analyzes the prevention of arthritis by swimming. If you rely on the traditional retrieval of clinical literature on the treatment of knee osteoarthritis with traditional Chinese medicine and internal medicine, you will find a lot of disordered data. It takes a lot of manpower and material resources to sort out the summary, and at this time, the role of data mining (DM) technology is brought into play. In this paper, the relevant information of the literature that meets the requirements is established in an Excel database, and the data of the relevant information is entered. Through sorting and analysis, the TCM syndrome types of knee osteoarthritis are summarized. Then, DM technology was used to carry out statistical analysis of frequency and prescription, to summarize the distribution characteristics of the corresponding knee osteoarthritis, TCM syndrome types, and the weight of each syndrome type, and to make a preliminary discussion at the same time. Finally, it is concluded that there are better prevention methods for arthritis in the research methods of traditional Chinese medicine. DM technology has been increasingly applied to all aspects of traditional Chinese medicine. DM technology has improved its research efficiency by 38% and achieved great results, which will play a greater role in promoting the research process of TCM syndrome.

A Two-Parameter Fractional Tsallis Decision Tree

Decision trees are decision support data mining tools that create, as the name suggests, a tree-like model. The classical C4.5 decision tree, based on the Shannon entropy, is a simple algorithm to calculate the gain ratio and then split the attributes based on this entropy measure. Tsallis and Renyi entropies (instead of Shannon) can be employed to generate a decision tree with better results. In practice, the entropic index parameter of these entropies is tuned to outperform the classical decision trees. However, this process is carried out by testing a range of values for a given database, which is time-consuming and unfeasible for massive data. This paper introduces a decision tree based on a two-parameter fractional Tsallis entropy. We propose a constructionist approach to the representation of databases as complex networks that enable us an efficient computation of the parameters of this entropy using the box-covering algorithm and renormalization of the complex network. The experimental results support the conclusion that the two-parameter fractional Tsallis entropy is a more sensitive measure than parametric Renyi, Tsallis, and Gini index precedents for a decision tree classifier.

Online Learning Behavior Feature Mining Method Based on Decision Tree

This research mainly discusses the design of online learning behavior feature mining method based on decision tree. Data collection is the real-time collection of online learning behavior data from distance learning websites. OWC (Office Web Component) technology is used to draw real-time charts on the page. Online learning students are selected as the research object, and the student's system log data and questionnaire data are selected. When combining the pre-pruning method and the post-pruning method to make decisions after the tree is pruned, the same source data is used to adjust, test and evaluate the decision tree model. The evaluation process to generate a complete decision tree is completed by the c4.5tree algorithm in C4.5, which can be named with a suffix of .names The type definition file is used to record the type of each attribute item or the range of possible values. In the study, the prediction accuracy rate of predicting learning effect based on "online learning behavior" reached more than 66%.

Entropic Analysis of Votes Expressed in Italian Elections between 1948 and 2018

In Italy, the elections occur often, indeed almost every year the citizens are involved in a democratic choice for deciding leaders of different administrative entities. Sometimes the citizens are called to vote for filling more than one office in more than one administrative body. This phenomenon has occurred 35 times after 1948; it creates the peculiar condition of having the same sample of people expressing decisions on political bases at the same time. Therefore, the Italian contemporaneous ballots constitute the occasion to measure coherence and chaos in the way of expressing political opinion. In this paper, we address all the Italian elections that occurred between 1948 and 2018. We collect the number of votes per party at each administrative level and we treat each election as a manifestation of a complex system. Then, we use the Shannon entropy and the Gini Index to study the degree of disorder manifested during different types of elections at the municipality level. A particular focus is devoted to the contemporaneous elections. Such cases implicate different disorder dynamics in the contemporaneous ballots, when different administrative level are involved. Furthermore, some features that characterize different entropic regimes have emerged.

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree

Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning. The entropy kernel potentially links its deviation and decision tree classification performance. This paper presents a decision tree learning algorithm based on constrained gain and depth induction optimization. Firstly, the calculation and analysis of single- and multi-value event uncertainty distributions of information entropy is followed by an enhanced property of single-value event entropy kernel and multi-value event entropy peaks as well as a reciprocal relationship between peak location and the number of possible events. Secondly, this study proposed an estimated method for information entropy whose entropy kernel is replaced with a peak-shift sine function to establish a decision tree learning (CGDT) algorithm on the basis of constraint gain. Finally, by combining branch convergence and fan-out indices under an inductive depth of a decision tree, we built a constraint gained and depth inductive improved decision tree (CGDIDT) learning algorithm. Results show the benefits of the CGDT and CGDIDT algorithms.

A Novel Consistent Random Forest Framework: Bernoulli Random Forests

Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been criticized. In this paper, a novel RF framework is proposed, named Bernoulli RFs (BRFs), with the aim of solving the RF dilemma between theoretical consistency and empirical performance. BRF uses two independent Bernoulli distributions to simplify the tree construction, in contrast to the RFs proposed by Breiman. The two Bernoulli distributions are separately used to control the splitting feature and splitting point selection processes of tree construction. Consequently, theoretical consistency is ensured in BRF, i.e., the convergence of learning performance to optimum will be guaranteed when infinite data are given. Importantly, our proposed BRF is consistent for both classification and regression. The best empirical performance is achieved by BRF when it is compared with state-of-the-art theoretical/consistent RFs. This advance in RF research toward closing the gap between theory and practice is verified by the theoretical and experimental studies in this paper.

2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm

Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization algorithm (ACO) and differential evolution algorithm (DE) are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA). The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation.