1: Introduction

Thanks to digital technologies we can extract knowledge from data by attributing intelligence to objects, through a connection between them. This new way of thinking about data has revolutionized processes and services, providing a new reading key to information from various sectors, so much so that the world of health has also been affected by this revolution (Ward, 2013; Mosavi et al., 2019). The healthcare sector is characterized by a series of problems and critical issues that have often affected its efficiency, offering patients a poor-quality service with significant costs (Hassan et al., 2019). To tackle this problem, a clear transformation of the system was required that would make it possible to respond to requests for improvement in the service provided (Wan, 2006; Beam et al., 2020).

The use of Information Technology (IT) in healthcare products, services, and processes accompanied by organizational changes and the development of new skills has introduced significant improvements in efficiency and productivity in the healthcare sector, as well as greater economic and social value of the health (Wan et al., 2020). IT is applied to medicine for personal care, which is at the center of the therapeutic, diagnostic, or preventive project, and as such receives or requires medical, health, or socio-sanitary acts (Dua et al., 2014; Liyanage et al., 2019).

Personal care, understood as the relationship between the person and the health system, has not changed. What has undergone is a clear transformation by the way in which health care is provided, both in terms of performing a medical service and in terms of organizing related services. The introduction of Information Technology has made health care more convenient from an economic point of view, as it reduces waste and inefficiencies with greater citizen involvement (Van Calster et al., 2019). IT allows a substantial reduction in the consumption of resources, both for the healthcare professional and for the citizen-user. The increase in productivity derives from the reduction of medical errors, from the attenuation or elimination of unnecessary treatment, from the reduction of waiting lines, from the limitation of the movements of citizens in the territory, from the reduction of waiting lists, and from the simplification of access to patient data and facilitating disease treatment (Siau and Shen, 2006; Kwak and Hui, 2019; Jewell et al., 2020).

A digital report without leaving home, paying for a specialist reservation without going to the office, or changing your doctor from the comfort of your PC are just some examples of improvements introduced using new technologies. The use of IT allows a tracking of operations to guarantee respect for privacy and the tracking of any access to clinical documents (Steil et al., 2019; Lanzing, 2019). The economic convenience introduced by the use of IT is closely connected with the increase in productivity and derives from the reduction of medical errors, from the mitigation or elimination of unnecessary treatment, through greater communication between the different healthcare institutions and the same professionals (Gu et al., 2019; Iftikhar et al., 2019). Another saving is obtained by reducing and/or eliminating paper material (Usak et al., 2020).

In this work, we used ensemble methodologies to preventively diagnose endocrinologic disorders such as those related to the thyroid. The data used as input derive from tests carried out on a population sample with the collection of numerous indicators. The term ensemble refers to a set of basic learning machines whose predictions are combined to improve the overall performance. The ensemble methods can be divided into two categories: generative and nongenerative. The nongenerative ones try to combine in the best possible way the predictions made by the machines, while the generative ones generate new sets of learners, to generate differences between them that can improve the overall performance. An ensemble method is a technique that combines predictions from multiple machine-learning algorithms to make predictions more accurate than any single model. By using multiple methods in modeling, prediction skills are improved as each contribution seeks to reinforce the weaknesses of the others.

The first part of the paper introduces the basics of the techniques based on Machine Learning with particular attention to the methods used in the field of Medical Informatics. Subsequently, the main algorithms based on Ensemble Learning are treated in detail and then move on to a rich review of the main works that have used a Machine Learning-based model for disease diagnosis. Finally, a specific case is treated: Predicting thyroid disease using ensemble learning.

2: Data analytics

Health care supported by digital technologies has generated in recent years a large volume of useful data on the clinical history of patients, on the treatment plans to which they have undergone, on the costs that the therapies applied have produced, and finally on the insurance coverage which the patients enjoyed. This amount of data has attracted the attention of data analysis experts who have been concerned with developing methodologies to analyze them (Zikopoulos and Eaton, 2011).

Data Analytics are tools that are based on statistical inference to examine in depth the raw data and knowledge available to identify correlations, trends or verify existing theories and models. They answer questions precisely and start from hypotheses formulated from the beginning, focusing on sectors, with the aim of obtaining the best practices that lead to an improvement of the system. These tools allow you to make future forecasts and what-if simulations to verify the effects of certain changes on the system, as well as to obtain more detailed and in-depth analyses. Using more sophisticated techniques and tools, they can analyze much larger datasets. Analysis tools help analysts turn data into knowledge. The ability to analyze a large amount of information, often unstructured, represents a clear source of competitive advantage and differentiation. Big Data, combined with sophisticated data analysis, have the potential to provide researchers with unprecedented insights into patient behavior and health system conditions, enabling decisions to be made faster and more effectively (Raghupathi and Raghupathi, 2014).

The data analysis examines the data with the aim of extracting knowledge from the information. In emergency assistance, data analysis helps emergency teams to efficiently select raw data, message traffic and news feeds from the Internet to instantly define where and when a health emergency is occurring. In preventive care, data analysis identifies outbreaks, trends, and prepares health specialists for the challenges they will face in the future (Kambatla et al., 2014).

Medical research also benefits greatly from data analysis. The ability to collect research, filter results, and stay abreast of the latest research-based best practices helps teams collaborate, improve test methods, and successfully apply for grants based on updated needs and information. Data analysis is more than just a hypothesis, but a determination of future events based on current facts and trends. Data analysis can be divided into two different spectra: exploratory data analysis and confirmation data analysis (Palanisamy and Thirunavukarasu, 2019). Exploratory data analysis, also known as EDA, is used to determine new trends in a market or sector. The analysis of confirmation data, or CDA, is used to demonstrate or refute existing hypotheses. In the medical field, the CDA is used in various sectors, from identifying the origin of a specific disease to which common drugs are most useful in the treatment of current symptoms. This is generally the way new drugs are developed, where research over time uses a combination of products and drugs to test and improve treatments. After years of research and combined data, the researcher can perform a complete analysis of the data, to determine whether the medical combination can cure the disease (Martinez et al., 2010).

Effective health analysis requires much more than extracting information from a database, applying a statistical model, and passing results to various end users. The process of transforming the data acquired in the source systems into information used by the health organization to improve quality and performance requires specific knowledge, adequate tools, quality improvement methodologies, and management commitment. Healthcare transformation efforts require decision makers to use information to understand all aspects of an organization’s performance. In addition to knowing what happened, decision makers now need information on what is likely to happen, what the organization’s improvement priorities should be, and what the expected impacts of the process and other improvements will be. Simply producing reports and visualizations of data from the health data repository is not enough to provide the information decision makers need (Reddy and Aggarwal, 2015).

Data Analytics can help decision makers achieve understanding of quality and operational performance by transforming the way information is used and decisions are made across the organization. Data Analytics is the system of tools and techniques necessary to generate understanding of data. The effective use of analysis within an organization requires that the necessary tools, methods, and systems have been applied appropriately and consistently and that the information generated by the analysis is accurate, validated, and reliable (Strome and Liefer, 2013).

3: Machine learning

Recently, a new tool for knowledge extraction has appeared in the panorama of Data Analytics: It is Machine Learning, a class of algorithms that using optimization techniques manage to retrieve useful information from data automatically. Machine Learning is a branch of Artificial Intelligence that includes all the studies on algorithms capable of performing a task with better performance as the experience grows. What makes this field extremely innovative is that it makes the machine an entity capable of carrying out inductive reasoning based on experience, in a completely analogous way to how man himself does it (Alpaydin, 2020).

Therefore, based on a training set of data from a certain probability distribution, the machine must be able to deal with new problems, therefore not known a priori, by building a probabilistic model of the occurrence space (Ciaburro, 2020). The computational analysis of machine learning algorithms is one of the works performed in learning theory, a field of study that aims to solve with various approaches, although never being able to offer certainties about the results, both for the finished quantity of data for training and for any underfitting and overfitting problems, essentially due to a disproportion between the required parameters and the number of observations (Marsland, 2015).

With the progress of studies and with the recognition of the various facets of the problems faced, there is a subdivision of the Machine Learning field into various branches that differ in the approach to solving the problems faced, for the type of data processed and for the task to be performed by the algorithm (Ciaburro and Venkateswaran, 2017).

Several paradigms have been developed, based on which to classify this type of algorithm:

• • Supervised learning: The model is trained by collecting input data, to then obtain outputs that allow one to formulate a general rule to correctly associate inputs and outputs.
• • Unsupervised learning: The model takes unlabeled inputs as an input and tries to generate a structure common to these input data.
• • Reinforcement learning: The model interacts with a dynamic environment and is notified or rewarded only if the goal to be accomplished is accomplished.

In this field there are numerous approaches, which are based both on the type of strategies adopted and on the models generated. Moving from decision trees, graphs that allow decision-making through paths that lead to the prediction of a given variable by classification, to genetic algorithms, algorithms that emulate the phenomenon of natural selection and genetic evolution through techniques such as mutation and crossover, from inductive logic programming, an approach that links propositional logic to symbolic learning and that makes extensive use of entailment starting from knowledge bases, to Bayesian networks, graphical representations of the dependency relationships between the variables of a system that provide a specific of any complete joint probability distribution. Among the algorithms based on Machine Learning, ensemble learning has proved particularly effective in supervised classification (Ciaburro, 2017).

4: Approaching ensemble learning

Artificial intelligence studies the reproducibility of complex mental processes through the use of computers and pays particular attention to how they perceive the external environment, how they interact with it, and how they are able to learn and solve problems, elaborate information, and reach decisions. Of great importance is the learning activity, which allows to increase the knowledge of the machine and to make adaptive changes, so that the decision-making process in a later period is more efficient (Polikar, 2012).

Therefore, the realization of an inductive as well as deductive learning is important, that is, a process that starting from a collection of examples concerning a specific sphere of interest, he arrives at the formulation of a hypothesis capable of predicting future examples. The conceptual difficulty in formulating a hypothesis consists in the impossibility of establishing whether it is a good approximation of the function that it must emulate or not, but it is possible to draw qualitative considerations, so a hypothesis that will be able to make a good generalization, so he will be able to correctly predict examples he has not seen so far. Furthermore, it is possible to arrive at the formulation of several hypotheses, with similar predictive capacity, all consistent. In this case, the optimal choice will be the simplest solution (Zhang and Ma, 2012).

A substantial difference between deductive and inductive inference is that the former guarantees logical correctness, as it does not alter the reality of interest, while the latter can tend toward excessive generalization and therefore carry out incorrect selective processes. Artificial intelligence systems are reflected in a vast domain of applications concerning a high quantity and heterogeneity of sectors, from the interpretation of natural language, to games, to the demonstration of mathematical theorems, to robotics. Furthermore, machine learning has great relevance in Data Mining, that is, the extraction of data and knowledge from an enormous wealth of information through automated methods. To reproduce typical activities of the human brain, such as the recognition of shapes and figures, the interpretation of language, and the perception of the environment in a sensorial way, neural networks have been created, with the aim of simulating the functioning of the animal brain on the computer (Krawczyk et al., 2017).

Ensemble learning methods are very powerful techniques for obtaining more correct decision-making processes, at the expense of greater complexity and a loss of interpretability, compared to learning systems based on single hypotheses. The ensemble learning combines the predictions of hypothesis collections to obtain greater performance efficiency. For explanatory purposes, it is useful to compare this type of learning to an executive committee of a company, in which several people with certain skills present their ideas to reach a final decision. It is evident that the knowledge of a group of people can lead to a more thoughtful solution than the decision of a single director. Also, this type of analogy allows us to make qualitative considerations that will be applicable in the domain of artificial intelligence (Gomes et al., 2017).

In fact, it is easy to imagine that if the directors all have very similar knowledge and therefore limited to the same area, the analyses and decisions of the individuals will not be very heterogeneous and we will obtain benefits to a lesser extent in the use of the ensemble. If, on the other hand, the knowledge of each director is highly sectored, so that each participant on the committee adds knowledge that is not replicated, the final decision will be more appropriate. If we assume that there is a director at the board’s management, who once had taken into consideration the individual opinions (votes), comes to a solution, it is reasonable to think that he trusts most of those who made more correct choices in the past, in the case I therefore practice advisors who he deems most reliable (Wang et al., 2016).

Similarly, in committee learning, the votes of the individual hypotheses are weighted to emphasize the predictions of those deemed most correct. In conclusion, ensemble learning combines different models derived from the same training set to produce a combination of them that widens the space of hypotheses. The term ensemble means a set of basic learning machines whose predictions are combined to improve overall performance (Akyuz et al., 2017).

Ensemble methods can be divided into two categories: generative and nongenerative. The nongenerative ones try to combine the predictions made by the machines in the best possible way, while the generative ones generate new sets of learners, to generate differences among them that can improve overall performance.

As for nongenerative techniques, in the classification, for example, the technique of major voting is used, possibly refined by weighing the votes proposed by the machines. Predictions can be combined by means of possibly weighted, median, product, sum, or by choosing the minimum or the maximum. Generative methods attempt to improve system performance by attempting to use diversity between learners. To do this, different sets are generated with which to train the machines using the resampling technique, or the aggregation of classes is manipulated differently through the feature selection technique, or even learners can be trained who specialize on specific parts of the set of learning with the mixture of expert techniques. Resampling techniques, such as bootstrapping, allow you to generate new sets from the original one (Wang et al., 2018).

The most common and efficient ensemble learning methods are:

• • bagging
• • boosting
• • stacking

However much they can improve the actual performance of the system, it is necessary to take into account a greater difficulty of analysis, since they can be composed of a substantial number of single models, which is why it will be difficult to intuitively understand which factors contributed to the improvement and which ones instead can be considered redundant or degrading.

For example, several decision trees generated on the same dataset could be considered and a vote by each of them on the classification of new data. The final predictive capacity will easily be more correct than that of the individual models and this could be significantly affected even by a subset of the trees involved in the choice, but intuitively it will be difficult to recognize this subset (Ciaburro and Iannace, 2020).

The simplest technique for combining the responses of individual classifiers into a single prediction is that of weighted votes and is used by both boosting and bagging, the substantial difference of which is in the way in which these models are generated (Alam et al., 2019).

5: Understanding bagging

Bagging is an ensemble method that takes its name from the union of the words Bootstrap AGGregatING and provides for the assignment of identical weights for each individual model applied to a specific set of training. It may be thought that if several training sets of equal cardinality are extracted starting from the same domain of a problem and a decision tree is built for each one, then these trees will be similar and will arrive at identical predictions for a new test example. Bootstrapping consists of the extraction with replacement of its elements to create new training sets that are different from each other. The probability of extraction of each example, in bagging, is equal to that of the others. The basic algorithm involves the creation of models for each training set and subsequently the combination of the various predictions on the test set through an average operation (Baskin et al., 2017).

This assumption is usually incorrect due to the instability of the decision trees, which owing to small changes in the input attributes can correspond to large changes in terms of ramifications and therefore lead to different classifications. It is implicit that if starting from the same basic set, one achieves remarkably different results, then the outputs can be both correct and wrong. In a system where there is a hypothesis for each training set and whose response to a new example is determined by the votes of the individual hypotheses (majority vote), a correct prediction will be more necessary than that which we would obtain starting from a single model. Nonetheless, it will always be possible to find an incorrect answer, as no learning scheme is affected by error. An estimate of the expected error in the architecture assumed can be calculated as the average of the errors of the individual classifiers. To better understand the characteristics of bagging, it is good to analyze bias errors, variance errors, and bootstrap first (Yaman and Subasi, 2019).

Given the following initial dataset:

$C=\left.\left.x1;y1\right),\dots ;(\mathit{xn};\mathit{yn})\right\}$

A few new datasets C_k (with k = 1, …, m) are extracted from the set C using the replacement technique. For each C_k dataset obtained, a predictive model is elaborated, according to the following function:

$f\left.x,C\right)=\frac{1}{m}\sum _{i=1}^m{f}_k\left.x,C_k\right)$

The algorithm brings improvements, thanks to the diversity of the various f_k models (Fig. 1). For this reason, less stable basic models are recommended, that is, capable of producing consistent differences also starting from similar training sets. This does not mean that the basic machines must necessarily be different. Bagging improves overall prediction performance because it reduces variance if the machines that are part of it have low bias (Subasi and Qaisar, 2020).

Recall that in an algorithm based on machine learning, we can identify two error components: the first due to the particular learning algorithm used, called bias, and the second relating to the peculiar training set used, indicated with the name of variance. The bias represents a deviation from the current value and cannot be calculated accurately, but can only be approximate, it is also independent of the number of training sets used and is an indicator of the persistence of the error of a specific algorithm.

The variance, on the other hand, is closely related to the training set used and is a measure of the variability of the learning model. In practical terms, if we use different training sets to repeat the training several times, the variance will be the difference between the values predicted by each model.

The choice of the type of architecture to be used in bagging is based on the evaluation of the best performance obtained on the test set: An evaluation of the error of the basic machines in terms of bias and variance can be carried out. Mediating low bias models allows you to obtain a low bias model by reducing the variance. Although the bias-variance analysis of the machines has not been addressed, the choice to consider the model with better performance on the test set proves to be consistent (Ditzler et al., 2017).

You can further criticize the choice by thinking that the best machines are optimal for the original training set and it is not known if they can be used for the various sets generated by the random extraction. The hope is that the best ones have characteristics of complexity suitable for the characteristics of the data to be approximated.

As for the dimensions of the C_k sets, there is no theory that indicates what the optimal value should be. This depends on the quantity and type of data available. Bagging is a parallelizable algorithm since the training of a single machine does not influence in any way that of the others.

This bootstrap aggregation technique tends to neutralize the instability of the learning algorithms and is more useful precisely in learning schemes where there is a high instability, as this implies a greater diversity obtainable with small input variations. Consequently, when bagging is used, attempts are made to make the learning algorithm as unstable as possible. The operation of this method can be summarized in two steps:

1. 1. the generation of individual models, in which m instances are selected from the training set and the learning algorithm is applied
2. 2. the combination of the models obtained to produce a classification

The error due to the bias turns out to be the mean square deviation obtained by averaging the numerical classifications of the individual models, while the variance is the distance between the predictions of the individual models and depends on the training set used. Bias and variance are closely related to the complexity of the learning model, so as the parameters added to it increase, the bias component will decay while the variance component will increase exponentially (Singhal et al., 2018).

6: Exploring boosting

The basic idea of the Boosting techniques is to build a list of classifiers by assigning, in an iterative way, a weight to each new classifier. Considering, in this way, its ability to recognize samples not correctly identified by the other classifiers already involved in the training. At each phase of the algorithm, a new classifier is trained using the dataset, in which the weighted coefficients are adjusted based on the performance of the previously trained classifier, so as to assign a greater weight to the incorrectly classified data points (Liu et al., 2018).

The algorithm focuses on the most difficult samples to classify, which are therefore weighted more. The final classifier is obtained with a weighted vote of the built models. As with other ensemble techniques, combining multiple models is particularly effective when they achieve a high percentage of correct predictions and are quite different from each other, i.e., presenting a high rate of variability. The ideal situation to which boosting aims is the maximum sectorization of the models, so that each of them is a specialist in a part of the domain in which the other classifiers fail to arrive at accurate predictions. Therefore, boosting attributes greater weight to instances that have not been correctly predicted, to build high models, in subsequent iterations, capable of filling this gap. In analogy with bagging, only learning algorithms of the same type are combined and their outputs are combined by vote or by averaging the individual responses, in the case of classification or numerical prediction, respectively (Ghojogh and Crowley, 2019).

The algorithm consists of the following steps:

1. 1. A tree is produced with a process that considers instances with greater weight to be more relevant
2. 2. The product tree is used to classify the training set
3. 3. The weight of instances correctly classified is reduced, while that of instances incorrectly classified is increased
4. 4. Steps 1 to 3 are repeated until a specified number of trees have been produced
5. 5. A weight proportional to its performance on the training set is assigned to each tree.

For the classification of new instances, a weighted voting system is used, usually majority voting, by all trees. The purpose of this algorithm is to produce different trees, to cover a wider set of types of instances. The defect with which the boosting method is often accused is the susceptibility to noise. If there are incorrect data in the training set, the boosting algorithm will tend to give greater weight to the instances that contain them, inevitably leading to a deterioration in performance. To overcome this problem, boosting algorithms have also been proposed, in which instances that are repeatedly classified incorrectly are interpreted as containing incorrect data and consequently their weight is reduced (Ng et al., 2018).

7: Discovering stacking

Stacked generalization, from whose abbreviation the term stacking derives, is the most recent ensemble technique, conceived to generate a scheme that minimizes the error rate of classifiers. Stacking, by virtue of its functioning, can be considered as a process that evolves the behavior of cross-validation to combine different individual models more efficiently. Stacking is a technique used to obtain high precision in generalization. This method tries to evaluate the reliability of the trees produced and is usually used to combine trees produced by different algorithms. The idea is to extract a new dataset containing an instance for each instance of the original dataset, in which however the original attributes are replaced with the classifications produced by each tree, while the output remains the original class. These new instances are then used to produce a new classifier that combines the different predictions into one. It is suggested to divide the original training set into two subsets. The first used to create the dataset and the second used to produce the base classifiers (Divina et al., 2018).

The new classifier’s predictions will therefore reflect the actual performance of the basic induction algorithms. While classifying a new instance, each base tree produces its prediction. These predictions will constitute a new instance which will be classified by the new classifier. If trees produce a probability classification, it is possible to increase the stacking performance by inserting in the new instances the probabilities expressed by each tree for each class. Stacking performance has been shown to be at least comparable to that of the best classifier chosen by cross-validation (Sun and Trevor, 2018).

8: Processing drug discovery with machine learning

Drug research and development is an expensive, long, and inefficient process, which takes more than 10 years to transfer a new drug to the market, with a cost of several billion dollars and a high risk of failure. The need to overcome the limits in the development of new therapies is made even more evident in the light of the global health needs’ data. Half of the failures are due to lack of efficacy, while a quarter of the failures are due to problems of tolerability, both causes expressing the difficulty of selecting the right target for the disease under study (Stephenson et al., 2019). To optimize the process of discovery and development of new therapeutic molecules, it is therefore necessary to use the knowledge hidden in the complexity of the data made available by biomedical research in the most efficient way. However, thanks to the computational skills and methodologies of computer science and machine learning techniques, it is possible to manage and analyze the volume of biomedical data in an automated way, to extrapolate significant relationships, generate new hypotheses to be subjected to experimental verification, and predict with statistical method the occurrence of future phenomena, including efficacy and toxicity associated with drugs (Vamathevan et al., 2019).

The search for new drugs is long and complex. First, some molecule candidates for therapy are identified and tested on cell cultures. Those that are most effective go to the next stages, in vivo tests. Finally, clinical trials are conducted on human patients. This process often requires years of research and significant economic investments. In recent times, the development of technology is revolutionizing the process of finding new drugs (Ekins et al., 2019).

In most cases, the targets of drug action are functional proteins such as receptors, enzymes, transport proteins, and a cascade of intracellular events derived from the drug-substrate interaction that culminates in the final biological effect. In a smaller number of cases, the methods of interaction between drug and living matter are carried out differently without affecting macromolecular complexes. Finally, some categories of drugs interact directly with DNA (Klambauer et al., 2019).

Generally, the birth of a drug starts precisely from the identification of a pharmacological target in the context of a clinical condition of interest. Once a project has been planned for the creation of a new drug starting from a real therapeutic need, researchers must focus attention on a pharmacological target to treat a pathological condition. Thus, begins the process of developing a drug, a long journey in stages that requires the use of huge human and economic resources (Xiao and Sun, 2019).

In this long process, the use of artificial intelligence can play a crucial role in speeding up the process by identifying the essential characteristics and leaving out the superfluous ones. Machine learning can be of assistance in many activities used in the modeling of new drugs. Identification of protein sequences, virtual screening, prediction of bio activators, chemical synthesis, and prediction of toxicity are just some examples of activities that can be dealt with taking the help of algorithms based on Machine Learning (Zoffmann et al., 2019).

9: Conclusion

In this chapter, we have studied ensemble learning algorithms and how these algorithms can be used for the classification of health disorders. Ensemble learning methods provide decision-making processes with superior performance, compared to basic methods. This improvement in performance is due to greater complexity and a loss of interpretability compared to learning systems based on individual hypotheses. Learning the ensemble combines the predictions of hypothesis collections to achieve greater performance efficiency. We initially introduced the topic by analyzing various bibliographic contributions that used Machine Learning-based models in the context of HealthCare. Subsequently, we deepened the methodologies underlying Ensemble learning by showing different algorithms. Finally, we applied these methods to classify thyroid problems.

The techniques based on Ensemble Learning record ranking results superior to those of other algorithms, which recommends their use. On the other hand, the models obtained with the use of these techniques are difficult to interpret, as the output of the model is difficult to explain. This makes such methodologies less popular in the business world. Furthermore, ensemble methods are difficult to learn for technicians and any wrong selection can lead to lower predictive accuracy than an individual model. Finally, the training process with such methodologies is expensive both in terms of computation time and memory space. These weaknesses represent starting points for improving technologies based on Ensemble Learning. They also pose challenges to the scientific community to spread the use of these technologies more widely in the world of work.