Topic
Decision tree model
About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.
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TL;DR: Wang et al. as mentioned in this paper developed a prediction model using decision tree model to identify factors associated with depression and compare the prediction performance of decision tree with that of logistic regression analysis, which had a much better performance in depression prediction.
Abstract:
Data analyses using artificial intelligence (AI) have not gained popularity in social work as much as other disciplines. To demonstrate its use, this study focused on Chinese older adults with neurodegenerative diseases (NDs) to (i) develop a prediction model using decision tree model to identify factors associated with depression and (ii) compare the prediction performance of decision tree model with that of logistic regression analysis. Decision tree model processing involved four stages: data collection, data preparation, model development, and result evaluation. An algorithm named Classification and Regression Trees (CARTs) was utilised to grow the decision tree by Python 3.7.1. The performance evaluation was based on accuracy, sensitivity, specificity and Goodness index (G). Seven factors grew the decision tree, including Instrumental Activities of Daily Living (IADLs), Mini-Mental State Examination (MMSE), Health status, Activity of Daily Living (ADL), Gender, Self-rated health change and Age. When compared to logistic regression, the decision tree model had a much better performance in depression prediction. Researchers, practitioners and policymakers need to focus on ways to decrease the vulnerability of depression in Chinese older adults with NDs. Also, the decision tree model can be applied as a referral to other physical or mental diseases prediction and analysis.
3 citations
TL;DR: The present decision tree model outperformed other clinical models, facilitating individual decision-making of adjuvant therapy after curative resection for primary cholangiocarcinoma.
Abstract: Background The aim of this study was to derive and validate a decision tree model to predict disease-specific survival after curative resection for primary cholangiocarcinoma (CCA). Method Twenty-one clinical characteristics were collected from 482 patients after curative resection for primary CCA. A total of 289 patients were randomly allocated into a training cohort and 193 were randomly allocated into a validation cohort. We built three decision tree models based on 5, 12, and 21 variables, respectively. Area under curve (AUC), sensitivity, and specificity were used for comparison of the 0.5-, 1-, and 3-year decision tree models and regression models. AUC and decision curve analysis (DCA) were used to determine the predictive performances of the 0.5-, 1-, and 3-year decision tree models and AJCC TNM stage models. Results According to the fitting degree and the computational cost, the decision tree model derived from 12 variables displayed superior predictive efficacy to the other two models, with an accuracy of 0.938 in the training cohort and 0.751 in the validation cohort. Maximum tumor size, resection margin, lymph node status, histological differentiation, TB level, ALBI, AKP, AAPR, ALT, γ-GT, CA19-9, and Child-Pugh grade were involved in the model. The performances of 0.5-, 1-, and 3-year decision tree models were better than those of conventional models and AJCC TNM stage models. Conclusion We developed a decision tree model to predict outcomes for CCA undergoing curative resection. The present decision tree model outperformed other clinical models, facilitating individual decision-making of adjuvant therapy after curative resection.
3 citations
Patent•
26 Oct 2018
TL;DR: In this paper, an intelligent financing service recommendation method and system is presented, where historical data is used for training a logistic regression model, a decision tree model and a support vector machine model; then the predicted probability values of the outputs of the three models are used as eigenvalues to be input into a neural network for performing secondary training.
Abstract: The invention relates to the technical field of financial service computers, and discloses an intelligent financing service recommendation method and system. According to the method, historical data is used for training a logistic regression model, a decision tree model and a support vector machine model; then the predicted probability values of the outputs of the three models are used as eigenvalues to be input into a neural network for performing secondary training; after the training is completed, the logic regression model, the decision tree model, the support vector machine model and theneural network form a multi-model fusion system so as to predict a success probability of matching a specific customer demand with a specific financing product; and the financing product is recommended to the customer demand according to the success probability, so that the recommendation success rate can be greatly improved.
3 citations
Posted Content•
TL;DR: In this paper, the authors introduce a new information theoretic measure called Public Information Complexity (PIC), which is a lower bound on communication complexity and an upper bound on information complexity.
Abstract: We introduce a new information theoretic measure that we call Public Information Complexity (PIC), as a tool for the study of multi-party computation protocols, and of quantities such as their communication complexity, or the amount of randomness they require in the context of information-theoretic private computations. We are able to use this measure directly in the natural asynchronous message-passing peer-to-peer model and show a number of interesting properties and applications of our new notion: the Public Information Complexity is a lower bound on the Communication Complexity and an upper bound on the Information Complexity; the difference between the Public Information Complexity and the Information Complexity provides a lower bound on the amount of randomness used in a protocol; any communication protocol can be compressed to its Public Information Cost; an explicit calculation of the zero-error Public Information Complexity of the $k$-party, $n$-bit Parity function, where a player outputs the bit-wise parity of the inputs. The latter result also establishes that the amount of randomness needed by a private protocol that computes this function is $\Omega(n)$.
3 citations
29 Oct 2002
TL;DR: In this paper, a formal model called R-COM-MTDP was proposed to analyze team formation and reorganization algorithms in multi-agent systems, which enables a rigorous and systematic analysis of complexity-optimality tradeoffs in team reformation approaches in different domain types.
Abstract: Recently researchers in multiagent systems have begun to focus on formal POMDP (Partially Observable Markov Decision Process) models for analysis of multiagent coordination. However, prior work has mostly focused on analysis of communication, such as via the COM-MTDP (Communicative Markov Team Decision Problem) model. This paper provides two extensions to this prior work that goes beyond communication and analyzes other aspects of multiagent coordination. In particular, we first present a formal model called R-COM-MTDP that extends COM-MTDP to analyze team formation and reorganization algorithms. R-COM-MTDP enables a rigorous and systematic analysis of complexity-optimality tradeoffs in team (re)formation approaches in different domain types. It provides the worst-case complexity analysis of the team (re)formation under varying conditions, and illustrates under which conditions role decomposition can provide significant reductions in computational complexity. Next, we propose COM-MTDP as a formal framework to analyze DCSP (Distributed Constraint Satisfaction Problem) strategies for conflict resolution. Different DCSP strategies are mapped onto policies in the COM-MTDP model, and agents compare strategies by evaluating their mapped policies. Thus, the two COM-MTDP based methods could open the door to a range of novel analyses of multiagent team (re)formation, and facilitate automated selection of the most efficient strategy for a given situation.
3 citations