BackgroundThere is evidence that direct journal endorsement of reporting guidelines can lead to important improvements in the quality and reliability of the published research. However, over the last 20 years, there has been a proliferation of reporting guidelines for different study designs, making it impractical for a journal to explicitly endorse them all. The objective of this study was to investigate whether a decision tree tool made available during the submission process facilitates author identification of the relevant reporting (...) guideline.MethodsThis was a prospective 14-week before–after study across four speciality medical research journals. During the submission process, authors were prompted to follow the relevant reporting guideline from the EQUATOR Network and asked to confirm that they followed the guideline (‘before’). After 7 weeks, this prompt was updated to include a direct link to the decision-tree tool and an additional prompt for those authors who stated that ‘no guidelines were applicable’ (‘after’). For each article submitted, the authors’ response, what guideline they followed (if any) and what reporting guideline they should have followed (including none relevant) were recorded.ResultsOverall, 590 manuscripts were included in this analysis—300 in the before cohort and 290 in the after. There were relevant reporting guidelines for 75% of manuscripts in each group; STROBE was the most commonly applicable reporting guideline, relevant for 35% (n = 106) and 37% (n = 106) of manuscripts, respectively. Use of the tool was associated with an 8.4% improvement in the number of authors correctly identifying the relevant reporting guideline for their study (p < 0.0001), a 14% reduction in the number of authors incorrectly stating that there were no relevant reporting guidelines (p < 0.0001), and a 1.7% reduction in authors choosing a guideline (p = 0.10). However, the ‘after’ cohort also saw a significant increase in the number of authors stating that there were relevant reporting guidelines for their study, but not specifying which (34 vs 29%; p = 0.04).ConclusionThis study suggests that use of a decision-tree tool during submission of a manuscript is associated with improved author identification of the relevant reporting guidelines for their study type; however, the majority of authors still failed to correctly identify the relevant guidelines. (shrink)

The coronavirus disease 2019 pandemic has affected most countries of the world. The detection of Covid-19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid-19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid-19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid-19 positive cases. The machine learning algorithms were (...) applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F-measure, precision, recall, area under the precision-recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees. (shrink)

Decision trees learning is one of the most practical classification methods in machine learning, which is used for approximating discrete-valued target functions. However, they may overfit the training data, which limits their ability to generalize to unseen instances. In this study, we investigated the use of instance reduction techniques to smooth the decision boundaries before training the decision trees. Noise filters such as ENN, RENN, and ALLKNN remove noisy instances while DROP3 and DROP5 may remove genuine instances. (...) Extensive empirical experiments were conducted on 13 benchmark datasets from UCI machine learning repository with and without intentionally introduced noise. Empirical results show that eliminating border instances improves the classification accuracy of decision trees and reduces the tree size, which reduces the training and classification times. In datasets without intentionally added noise, applying noise filters without the use of the built-in Reduced Error Pruning gave the best classification accuracy. ENN, RENN, and ALLKNN outperformed decision trees learning without pruning in 9, 9, and 8 out of 13 datasets, respectively. The datasets reduced using ENN and RENN without built-in pruning were more effective when noise was intentionally introduced in different ratios. (shrink)

This article investigates the propensity for academic dishonesty by university students using the partitioning method of decision tree analysis. A set of prediction rules are presented, and conclusions are drawn. To provide context for the decision tree approach, the partition process is compared with results of more traditional probit regression models. Results of the decision tree analysis complement the probit models in terms of predictive accuracy and confirm results previously found in the literature. In (...) particular, students’ moral character—whether they believe cheating is acceptable—is found to be the most important factor in determining the propensity for academic dishonesty. (shrink)

Despite the growing popularity of machine learning models in the cyber-security applications ), most of these models are perceived as a black-box. The eXplainable Artificial Intelligence has become increasingly important to interpret the machine learning models to enhance trust management by allowing human experts to understand the underlying data evidence and causal reasoning. According to IDS, the critical role of trust management is to understand the impact of the malicious data to detect any intrusion in the system. The previous studies (...) focused more on the accuracy of the various classification algorithms for trust in IDS. They do not often provide insights into their behavior and reasoning provided by the sophisticated algorithm. Therefore, in this paper, we have addressed XAI concept to enhance trust management by exploring the decision tree model in the area of IDS. We use simple decision tree algorithms that can be easily read and even resemble a human approach to decision-making by splitting the choice into many small subchoices for IDS. We experimented with this approach by extracting rules in a widely used KDD benchmark dataset. We also compared the accuracy of the decision tree approach with the other state-of-the-art algorithms. (shrink)

The figures visualizing single and combined classifiers coming from decision trees group and Bayesian parametric and nonparametric discriminant functions show the importance of diversity of bagging or boosting combined models and confirm some theoretical outcomes suggested by other authors. For the three medical sets examined, decision trees, as well as linear and quadratic discriminant functions are useful for bagging and boosting. Classifiers, which do not show an increasing tendency for resubstitution errors in subsequent boosting deterministic procedures loops, are (...) not useful for fusion, e.g. kernel discriminant function. For the success of resampling classifiers’ fusion, the compromise be- tween accuracy and diversity is needed. Diversity important in the success of boosting and bagging may be assessed by concordance of base classifiers with the learning vector. (shrink)

With the continuous development of “Internet + Education”, online learning has become a hot topic of concern. Decision tree is an important technique for solving classification problems from a set of random and unordered data sets. Decision tree is not only an effective method to generate classifier from data set, but also an active research field in data mining technology. The decision tree mining algorithm can classify the data, grasp the teaching process of the (...) teacher, and analyze the overall performance of the students, so as to realize the dynamic management of the educational administration and help the educational administration personnel to make the right decision, with more reasonable allocation of resources. This paper evaluates students’ academic performance based on the learning behavior data of online learning, so as to intervene in students’ learning in advance, which is the key problem that needs to be solved at present. Taking students’ learning attitude, completion of homework, and attendance as factors, the paper uses decision tree technology to analyze the factors affecting students’ performance, and evaluates students’ performance. Firstly, this paper collects the high-dimensional behavioral characteristic data of students’ online learning and conducts correlation analysis after preprocessing the behavioral characteristic data. Then, the decision tree C4.5 algorithm is used to construct a performance evaluation model. Students’ performance is evaluated by the model, and the evaluation accuracy is about 88% compared with actual performance. Finally, through the model analysis, it is concluded that the video task point completion is the most influential in students’ achievement, followed by chapter test completion and chapter test average score, and the course interaction amount and homework average score are the least influential in students’ achievement, which has a practical reference value for effectively serving online learning and teachers’ teaching. (shrink)

Game trees (or extensive-form games) were first defined by von Neumann and Morgenstern in 1944. In this paper we examine the use of game trees for representing Bayesian decision problems. We propose a method for solving game trees using local computation. This method is a special case of a method due to Wilson for computing equilibria in 2-person games. Game trees differ from decision trees in the representations of information constraints and uncertainty. We compare the game tree (...) representation and solution technique with other techniques for decision analysis such as decision trees, influence diagrams, and valuation networks. (shrink)

This paper examines feature selection for log linear models over rich constraint-based grammar (HPSG) representations by building decision trees over features in corresponding probabilistic context free grammars (PCFGs). We show that single decision trees do not make optimal use of the available information; constructed ensembles of decision trees based on different feature subspaces show signifi- cant performance gains (14% parse selection error reduction). We compare the performance of the learned PCFG grammars and log linear models over the (...) same features. (shrink)

The paper starts with the research on the early discovery of college students' psychological problems. Besides, it analyzes the data of the general survey of college students' mental health in a certain university, the existing data of students with psychological problems, and the questionnaire data of students' basic information in school. By comprehensively using the decision tree model and Kendall correlation analysis and other methods, using Python and SPSS software to preprocess the data and realize the model, it (...) can obtain a psychological problem prediction model based on the objective behavior data of college students. The model is actually analyzed, and it gets good results. (shrink)

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, (...) normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area. (shrink)

With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make (...) full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given. (shrink)

Physicians have developed a number of implicit and explicit approaches to complex medical decisions. Decision analysis is an explicit, quantitative method of clinical decision making that involves the separation of the probabilities of events from their relative values, or utilities. Its use can help physicians make difficult choices in a manner that promotes true patient participation. Decision analysis also provides a framework for the incorporation of data from multiple sources and for the assessment of the impact of (...) uncertain data on the final decision. Although this approach is imperfect, it represents a significant advance in clinical decision making. (shrink)

Previous studies have been split over how to view family owners’ CSR engagement, arguing that they either engage in or disengage from CSR based on different motives (i.e., preserving socio-emotional wealth vs. seeking rent expropriation). Focusing on family owners in business groups, this study integrates these divergent views. We hypothesize that family owners would pursue both motives simultaneously by optimizing the level of CSR of each affiliated firm depending on their ownership level. Furthermore, we argue that this tendency is moderated (...) by group-level contexts. Using a sample of Korean business groups, we found that family ownership is negatively associated with affiliated firms’ CSR. Also, this negative relationship is more pronounced when firms belong to business groups with more charitable corporate foundations and when business networks are greater in their scope and scale. This study contributes to the literature by offering a more complete understanding of how family owners make CSR decisions in business group contexts. (shrink)

The Tree of Nature represents an IT-based approach to understanding Nature in the light of present-day scientific knowledge. The universe, in this view, consists of discrete entities; these are not material particles, however, but information processing events that produce observable changes in the world. The surprising result of this analysis is that the workings of Nature are based on a decision tree consisting of two dozen parameters. The tree is similar to the evolutionary phylogenetic system of (...) the various forms of life, but it is far more fundamental; constituting, as it does, the very base of everything that exists. The overall conclusion is that the universe is steadily producing new information. Metaphorically speaking, it constitutes a self-unfolding story in which events do not pass away into non-existence but instead remain forever as an indelible part of the whole. This also holds true for integrated sets of events, such as the lifetime experiences of an individual. The Tree of Nature leads to the following testable prediction: A successful quantum-gravity theory will involve the notion of a strong complementarity between gravity and the strong nuclear force. (shrink)

Why do otherwise well-intentioned managers make decisions that have negative social or environmental consequences? To answer this question, the authors combine the literature on construal level theory with the compromise effect to explore the circumstances that lead to seemingly unethical decision-making. The results of two studies suggest that the degree to which managers make high-risk tradeoffs is highly influenced by how they mentally represent the decision context. The authors find that managers are more likely to make seemingly unethical (...) tradeoffs when psychological distance is high (rather than low) and when they are forced to choose between competing alternatives. However, when given the option not to choose, managers better reflect on the consequences of each alternative, and thus become more likely to choose options with less risk of negative consequences. The results suggest that simply offering managers the option not to choose may reduce psychological distance and help organizations avoid seemingly unethical decision-making. (shrink)

Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have (...) industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. These techniques include Credal Decision Tree, Cost-Sensitive Decision Forest, Decision Stump, Forest by Penalizing Attributes, Hoeffding Tree, Decision Tree, Logistic Model Tree, Random Forest, Random Tree, and REP-Tree. Performance of each technique is evaluated using different measures, i.e., mean absolute error, relative absolute error, root mean squared error, root relative squared error, specificity, precision, recall, F-measure, G-measure, Matthew’s correlation coefficient, and accuracy. The overall outcomes of this paper suggested RF technique by producing best results in terms of reducing error rates as well as increasing accuracy on five datasets, i.e., AR3, PC1, PC2, PC3, and PC4. The average accuracy achieved by RF is 90.2238%. The comprehensive outcomes of this study can be used as a reference point for other researchers. Any assertion concerning the enhancement in prediction through any new model, technique, or framework can be benchmarked and verified. (shrink)

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence (...) of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system. (shrink)

Modern scholarship on decision behavior dates from the late 1940s. But that scholarship has been preoccupied with two ideas that are much older. One is the notion of expected utility, first articulated in the scholarly literature by Daniel Bernoulli in 1738. In its simplest form, the expected utility concept applies to monetary gambles. Imagine you are asked to choose between two gifts, either gamble G, which you would then play and get either $9 or nothing, or else a simple, (...) direct payment of $3. As shown in the decision tree in figure 10.1, you receive gamble G's $9 payoff if the toss of a fair die yields one or two pips, but you receive nothing otherwise. (In decision trees, choice points are represented by square nodes, chance points by circular nodes.) The expected value or expectation of a gamble is the sum of its payoffs, each weighted (i.e., multiplied) by its probability. Thus, the expected value of G is EV(G) = (1/3)($9) + (2/3)($0) = $3, the same as the direct payment. So, if you generally make decisions like this one so as to maximize expected value, you would be indifferent between the present options. However, if you are like most people, you are not indifferent. Instead, you are risk‐averse, preferring the guaranteed $3. Why? (shrink)

As a way forward in assessing how the Old Testament wisdom tradition might speak to decisions in a modern medical context, in this paper, I propose exploring the iconographic function of the “tree of life” in the Old Testament, which is consistently associated with both wisdom as well as life and health, in order to tease out two-related issues that can help in providing a Christian theological framework for thinking about the problem of the medicalization of risk: first, how (...) should the natural and good human desire for health and long life be framed in terms of the pursuit of wisdom? And, second, how might a sapientially formed character approach risk and uncertainty in making medical decisions? The answers to these questions help establish a framework in which more specific questions related to the medicalization of risk can be assessed. As such, this paper is deliberately programmatic and perspectival rather than prescriptive. (shrink)

Many decisions involve multiple stages of choices and events, and these decisions can be represented graphically as decision trees. Optimal decision strategies for decision trees are commonly determined by a backward induction analysis that demands adherence to three fundamental consistency principles: dynamic, consequential, and strategic. Previous research found that decision-makers tend to exhibit violations of dynamic and strategic consistency at rates significantly higher than choice inconsistency across various levels of potential reward. The current research extends these (...) findings under new conditions; specifically, it explores the extent to which these principles are violated as a function of the planning horizon length of the decision tree. Results from two experiments suggest that dynamic inconsistency increases as tree length increases; these results are explained within a dynamic approach–avoidance framework. (shrink)

Reframing reveals possibilities. This article highlights how conceptual shifts regarding ‘sustainability’ occurring inside China’s municipalities and major corporations are opening the way for new collaborations with technology companies and technology artists. These shifts – from predetermined accounting to systems thinking – reveal new opportunities to intervene in the biophysical and economic challenges facing China today. In companies, this shift implies placing financially relevant environmental, social and governance (ESG) factors at the core of business strategy. In municipalities, this shift necessitates designing (...) and implementing systems that track ecosystem services rather than simply stocks of natural resources and recognizing interdependencies between these services and economic development. This article suggests that this sustainability reframing can draw heavily on systems thinking from environmental science, technology companies’ drive to develop products that meet these emerging demands, corporations’ need to compete in an uncertain developing market and the reframing skills of artists, particularly digital artists. This article concludes with opportunities for digital artists to intervene and collaborate with sustainability practitioners to open new directions for cities and companies in China. (shrink)

This article discusses interactive minimum cost spanning tree problems and argues that the standard approach of using a transferable utility game to come up with a fair allocation of the total costs has some flaws. A new model of spillover games is presented, in which each player’s decision whether or not to cooperate is properly taken into account.

There are at least two plausible generalisations of subjective expected utility (SEU) theory: cumulative prospect theory (which relaxes the independence axiom) and Levi’s decision theory (which relaxes at least ordering). These theories call for a re-assessment of the minimal requirements of rational choice. Here, I consider how an analysis of sequential decision making contributes to this assessment. I criticise Hammond’s (Economica 44(176):337–350, 1977; Econ Philos 4:292–297, 1988a; Risk, decision and rationality, 1988b; Theory Decis 25:25–78, 1988c) ‘consequentialist’ argument (...) for the SEU preference axioms, but go on to formulate a related diachronic-Dutch-book-style’ argument that better achieves Hammond’s aims. Some deny the importance of Dutch-book sure losses, however, in which case, Seidenfeld’s (Econ Philos 4:267–290, 1988a) argument that distinguishes between theories that relax independence and those that relax ordering is relevant. I unravel Seidenfeld’s argument in light of the various criticisms of it and show that the crux of the argument is somewhat different and much more persuasive than what others have taken it to be; the critical issue is the modelling of future choices between ‘indifferent’ decision-tree branches in the sequential setting. Finally, I consider how Seidenfeld’s conclusions might nonetheless be resisted. (shrink)

A decision tree is one of the machine learning techniques and also one of the major knowledge representations of data mining results.This is because it is easy to understand its meaning for human analysts.Even ID3, the representative algorithm, is known to exhibit remarkable performance deterioration under certain circumstances, particularly due to strong correlation between attributes representing the class of examples. One of the approaches to get more preferable decision trees is pre-processing the training data to extend its (...) description, such as attributes generation and attribute selection. There is also the idea of decision trees with a region rule. In this paper, we consider two approaches, i.e., decision trees with a region rule allowing multiple attributes, and a pre-processing method of a region rule to enabling any suitable number of attributes to correspond to branch nodes, where an optimal division condition with arbitrarily multiple attributes is acquired. By using this method, we propose a new decision tree generation algorithm guaranteeing to select effective compound attributes with each branch node, where an MDL-based new evaluation criterion is also defined for determining the optimal number of compound attributes specified to each node.This algorithm is applied to datasets containing only nominal values. It consists of three processes: compound attributes selection, parent node integration, and pruning. We call this new decision trees DTMACC (Decision Trees with Multiple Attributes Concept Clustering). The effectiveness and comprehensiveness of the proposed algorithm are confirmed through experiments comparing to the ordinary decision trees and an effective pre-processing method. (shrink)

The aim of this paper is to provide a decision procedure for Dummett's logic LC, such that with any given formula will be associated either a proof in a sequent calculus equivalent to LC or a finite linear Kripke countermodel.

BackgroundGenealogical research and ancestry testing are popular recreational activities but little is known about the impact of the use of these services on clients’ biological and social families. Ancestry databases are being enriched with self-reported data and data from deoxyribonucleic acid analyses, but also are being linked to other direct-to-consumer genetic testing and research databases. As both family history data and DNA can provide information on more than just the individual, we asked whether companies, as a part of the consent (...) process, were informing clients, and through them clients’ relatives, of the potential implications of the use and linkage of their personal data.MethodsWe used content analysis to analyse publically-available consent and informational materials provided to potential clients of ancestry and direct-to-consumer genetic testing companies to determine what consent is required, what risks associated with participation were highlighted, and whether the consent or notification of third parties was suggested or required.ResultsWe identified four categories of companies providing: 1) services based only on self-reported data, such as personal or family history; 2) services based only on DNA provided by the client; 3) services using both; and 4) services using both that also have a research component. The amount of information provided on the potential issues varied significantly across the categories of companies. ‘Traditional’ ancestry companies showed the greatest awareness of the implications for family members, while companies only asking for DNA focused solely on the client. While in some cases companies included text recommending clients inform their relatives, showing they recognised the issues, often it was located within lengthy terms and conditions or privacy statements that may not be read by potential clients.ConclusionsWe recommend that companies should make it clearer that clients should inform third parties about their plans to participate, that third parties’ data will be provided to companies, and that that data will be linked to other databases, thus raising privacy and issues on use of data. We also suggest investigating whether a ‘generational consent’ should be created that would include more than just the individual in decisions about participating in genetic investigations. (shrink)

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study (...) is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery. (shrink)