Waikato Environment For Knowledge Analysis Research Articles

Machine Learning in Small and Medium-Sized Enterprises, Methodology for the Estimation of the Production Time

Data mining (DM) and machine learning (ML) are widely used in production planning and scheduling. Their application to production time estimation leads to improved planning and scheduling accuracy, resulting in increased overall efficiency. Small and medium-sized enterprises (SMEs) often have a small amount of data, which results in the limited adoption of DM and ML. Instead, production time estimation is still performed using rough approximations, which are inaccurate and non-reproducible. Therefore, this article proposes an ML methodology for production time estimation. It is adapted to the needs of SMEs and is applied with limited data. The methodology is based on the categorization of four job types (from A to D), the partitioning of data according to the limit theorem of data convergence, and the definition of risk based on metrics of probability and statistics. ML was applied by WEKA Workbench (Waikato Environment for Knowledge Analysis). It is also integrated into the Cross Industry Standard Process for DM. The methodology was implemented on data from a medium-sized company, Schoepstal Maschinenbau GmbH, for job types A and B to estimate machine/job cycle time, manufacturing cycle time, and lead time. Different accuracies were obtained for individual estimation models, confirming the strong dependence of the models on data quality. Suitable models were found for the implementation of the estimation of the manufacturing cycle time and the machine/job cycle time. The modeling of lead time estimation was unsuccessful. This was due to the weak dependence between the learning values and the values of the selected model attributes. The implementation of the methodology for job types C and D is the subject of further research.

Enhancing stroke prediction using the waikato environment for knowledge analysis

<p>State-of-the-art data mining tools incorporate advanced machine learning (ML) and artificial intelligence (AI) models, and it is widely used in classification, association rules, clustering, prediction, and sequential models. Data mining is important for the process of diagnosing and predicting diseases in the early stages, and this contributes greatly to the development of the health services sector. This study utilized classification to predict the stroke of a sample of the patient dataset that was taken from Kaggle. The classification model was created using the data mining program waikato environment for knowledge analysis (WEKA). This data mining tool helped identify individuals most at risk of stroke based on analysis of features extracted from the patient’s dataset. These features were used in classification processes according to the naive Bayes (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. Analysis of the classification results of the previous algorithms showed that the SVM outperformed other algorithms in terms of accuracy (94.4%), sensitivity (100%), and F-measure (97.1%). However, the NB algorithm had the best performance in terms of precision (95.7%).</p>

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs. We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree. The data were further subjected to InfoGain feature selection to identify the most informative miRNA sequence and gene target descriptors. The ML models' abilities to distinguish between miRNA implicated in EC and control group miRNA was then tested. Of the tested WEKA classifiers, the top 3 performing ones were random forest, Hoeffding tree, and naïve Bayes. The TensorFlow Keras neural network model was subsequently trained and tested, the model's predictive power was further validated using an independent dataset. The TensorFlow Keras gave an accuracy 0.91. The WEKA best algorithm (naïve Bayes) model yielded an accuracy of 0.94. The results demonstrate the potential of ML-based miRNA classifiers in diagnosing EC. However, further studies are necessary to validate these findings and explore the full clinical potential of this approach.

Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms

Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage

Securing cloud-enabled smart cities by detecting intrusion using spark-based stacking ensemble of machine learning algorithms

<abstract> <p>With the use of cloud computing, which provides the infrastructure necessary for the efficient delivery of smart city services to every citizen over the internet, intelligent systems may be readily integrated into smart cities and communicate with one another. Any smart system at home, in a car, or in the workplace can be remotely controlled and directed by the individual at any time. Continuous cloud service availability is becoming a critical subscriber requirement within smart cities. However, these cost-cutting measures and service improvements will make smart city cloud networks more vulnerable and at risk. The primary function of Intrusion Detection Systems (IDS) has gotten increasingly challenging due to the enormous proliferation of data created in cloud networks of smart cities. To alleviate these concerns, we provide a framework for automatic, reliable, and uninterrupted cloud availability of services for the network data security of intelligent connected devices. This framework enables IDS to defend against security threats and to provide services that meet the users' Quality of Service (QoS) expectations. This study's intrusion detection solution for cloud network data from smart cities employed Spark and Waikato Environment for Knowledge Analysis (WEKA). WEKA and Spark are linked and made scalable and distributed. The Hadoop Distributed File System (HDFS) storage advantages are combined with WEKA's Knowledge flow for processing cloud network data for smart cities. Utilizing HDFS components, WEKA's machine learning algorithms receive cloud network data from smart cities. This research utilizes the wrapper-based Feature Selection (FS) approach for IDS, employing both the Pigeon Inspired Optimizer (PIO) and the Particle Swarm Optimization (PSO). For classifying the cloud network traffic of smart cities, the tree-based Stacking Ensemble Method (SEM) of J48, Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are applied. Performance evaluations of our system were conducted using the UNSW-NB15 and NSL-KDD datasets. Our technique is superior to previous works in terms of sensitivity, specificity, precision, false positive rate (FPR), accuracy, F1 Score, and Matthews correlation coefficient (MCC).</p> </abstract>

Improvement of Translation Accuracy for the Word Sense Disambiguation System using Novel Classifier Approach

Machine Translation (MT) is a crucial application of Natural language Processing (NLP). This MT technique automatic and based on computers. One of the most modern techniques adopted in MT is Machine Learning (ML). Over the past few years, ML has grown in popularity during MT process among researchers. Ambiguity is a major challenge in MT. Word Sense Disambiguation (WSD) is a common technique for solving the ambiguity problem. ML approaches are commonly used for the WSD techniques and are used for training and testing purposes. The outcome prediction of the test data gives encouraging results. Text classification is one of the most significant techniques for resolving the WSD. In this paper, we have analyzed some common supervised ML text classification algorithms and also proposed a “hybrid model” called “AmbiF.” We have compared the results of all analyzed algorithms with the proposed model “AmbiF. The analyzed supervised algorithms are Decision Tree (DT), Bayesian network, Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR). The range of accuracy for all the algorithms that were examined is between sixty-eight and eighty-four percent. To improve the accuracy of the AmbiF model, we have merged the DT, SVM, and Naïve Bayes (NB)-classifier approach. For testing the model, we have used the ten-fold cross-validation test method. The AmbiF model’s accuracy has been reported eighty-five percent. Comparing the AmbiF model to all other analyzed supervised ML classification algorithms, it has also demonstrated great precision, recall, and F-score. Waikato Environment for Knowledge Analysis (WEKA)’s ML-tool is used to analyze the algorithms and the AmbiF model.

Evaluating the Accuracy of Classification Algorithms for Detecting Heart Disease Risk

The healthcare industry generates enormous amounts of complex clinical data that make the prediction of disease detection a complicated process. In medical informatics, making effective and efficient decisions is very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely, J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the impact of the feature selection method. A comparative and analysis study was performed to determine the best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity and specificity. The importance of using classification techniques for heart disease diagnosis has been highlighted. We also reduced the number of attributes in the dataset, which showed a significant improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart disease was Random Forest with an accuracy of 99.24%.

Predicting Subcontractor Performance Using Artificial Intelligence

Subcontractors contribute to almost 90% of the overall construction work hence, assessing the performance of their work from commencement till the completion stage is essential. This paper focuses on identifying different factors affecting the subcontracted work performance and developing a predictive model using classification - based algorithm to find the proficient subcontractors.
 Data collected from the building construction projects was analyzed and utilized. Expert validation method was carried out to validate the factors that were obtained from literature review and a survey was conducted to assess the subcontractor’s performance level. Different classification algorithms such as Naïve Bayes, Logistic, Multilayer Perceptron, Sequential Minimal Optimization (SMO), KStar, J48 and Random Forest were applied to the collected data. Waikato Environment for Knowledge Analysis (WEKA), an opensource machine learning tool was used to compare the performance of several algorithms. Statics were generated and compared using k-folds cross validation (k=10) method.
 Among the seven algorithms/classifiers, Random Forest had the highest accuracy in schedule performance model and Multilayer Perceptron in quality performance model

Behavior Analysis and Clustering of Consumers Towards Halal Awareness

Due to Indonesia's large Muslim population, the issue of halal is contentious for Indonesians, and many restaurants in Indonesia do not have halal certification. This study investigates the various factors that influence customer behavior and identifies managerially implementable consumer clusters. This study used a quantitative methodology, focusing on a sample of consumers residing in the Greater Jakarta area. Lemeshow sampling was used to gather data from 196 respondents via an online survey. The analysis technique consisted of Structural Equation Modeling (SEM)-Partial Least Square and cluster analysis using the K-means algorithm with the Waikato Environment for Knowledge Analysis (WEKA). Subjective norms, perceived behavioral control, halal awareness, and intention factors have been proven to have positive and significant effects on consumer behavior when choosing a halal restaurant. The findings on consumer clusters that have evolved provide significant managerial implications. Assistance is required from halal institutions, businesses, and consumers with a high level of halal awareness to raise their awareness of halal.

Machine Learning-Based Energy Consumption Estimation of Wastewater Treatment Plants in Greece

Amidst a global discourse on energy resources, it is imperative to provide decision makers with a comprehensive overview of energy consumption (EC) associated with various projects, particularly wastewater treatment plants (WWTPs). Ensuring compliance with stringent effluent quality criteria in the treatment of municipal wastewater necessitates a substantial EC, representing a predominant factor contributing to the operational expenses incurred by WWTP. Machine learning (ML) techniques can contribute to the estimation of the WWTPs’ EC, which requires efficient and accurate data. This article uses data from several municipal WWTP projects in Greece, which are examined in order to produce EC estimation models. Data were first statistically analyzed, according to the context of project attributes and the context of EC, and correlation analysis identified the appropriate predictive project variables. Then, the attribute selection function in Waikato Environment for Knowledge Analysis 3.8.4 (WEKA 3.8.4) software emphasized the most effective subset of variables. The extracted variables from the combination of the correlation analysis and the WEKA attribute function were used as input neurons for the construction of neural network (NN) models, in the Fast Artificial Neural Network Tool 1.2 (FANN Tool 1.2). The optimum NN model resulted in a mean squared error (MSE) of 8.99899 × 10−5 and was based on treatment capacity, flow rate, influent load, and served population as its inputs. Notably, the research highlights the potential generalizability of these models in Greece and beyond the Greek context, offering valuable tools for stakeholders to inform decision making, allocate resources efficiently, and improve energy-efficient designs, resulting in cost savings and sustainability benefits.

Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed

The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew’s correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain.

Heart failure prediction framework using random forest and J48 with Adaboost algorithms

Heart failure is a very serious condition in health sector globally. It has proven difficult and expensive to manage over the years even with some pre-existing prediction models that signal its occurrence. The predictive accuracies of the existing models are below impressive hence the need for better heart failure predictive models. This work developed two heart failure predictive models to contribute to the decrease in the mortality rate due to heart failure as well as assist patients and physicians in managing the condition. The models were Random Forest(RF) and J48 model with AdaBoost. The dataset for the work was collected from the Cleveland Hospital database. It has 13 attributes and 303 instances. The dataset was preprocessed before use and was converted to the CSV format usable in the Waikato Environment for Knowledge Analysis (WEKA) software. The Agile Unified Process (AUP) methodology was adopted in this work the simulator for the work. The Simulator (web-based) was implemented using Python programming language and the Streamlit for python. The result of the models showed a 92.3% accuracy in prediction for the AdaBoosted J48 model and 89.2% for the Random Forest model. The results indicated that J48 with AdaBoost outperformed RF.

A comparative study of classification techniques in data mining algorithms used for medical diagnosis based on DSS

A significant amount of data is gathered by the healthcare sector, but it is not appropriately mined and utilized. Finding these hidden links and patterns is frequently underutilized. Our study focuses on this element of medical diagnostics by identifying patterns in the information gathered about kidney illness, liver disease, and chronic pancreatitis (CP) and designing adaptive medical decision support systems (MDSS) to assist doctors. This research compares a variety of data mining (DM) techniques, knowledge extraction tools, and software platforms for usage in a DSS for analysis using the Waikato environment for knowledge analysis (WEKA) mining tool (decision tree (DT)). The objective is to determine the most significant risk factors based on the extraction of the categorization criteria. The datasets used for this work are illustrates how successfully DM and DSS are integrated. In this research, we suggest using the C4.5 DT algorithm, Naïve Bayes (NB) algorithm, and the logistic regression (LR) algorithm to categorize these diseases and evaluate their performance and accuracy rates. It inferred that the C4.5 algorithm accuracy is 0.873% which is better than the other two algorithms in terms of rule generation and accuracy.

Application of supervised learning algorithm to determine the quality of slippers in WEKA

This study is driven by the objective of evaluating the effectiveness of various regression algorithms in the prediction of slipper quality. The selected regression algorithms were implemented within the Waikato Environment for Knowledge Analysis. The assessment of their performance was conducted through the analysis of correlation coefficients, providing insights into their predictive capabilities. Notably, the Random Forest algorithm demonstrated the highest predictive power with an impressive correlation coefficient (r=0.76), surpassing other models in the analysis. Following Random Forest, the k-nearest neighbor algorithm achieved a substantial correlation coefficient of (r=0.65), followed by the Decision Tree (r=0.53), Linear regression (r=0.51), and the Multi-layer perceptron (r=0.51). In contrast, the Support Vector Machine showed a notably lower correlation coefficient (r=0.51), indicating its comparatively weaker predictive performance. Furthermore, this study uncovered two variables, "Easy to Wash" and "Water Resistance," which displayed significant correlations of (r=0.49) and (r=-0.35), respectively, in relation to the predictive performance of the regression model. However, no significant correlation was observed for other variables. In light of these findings, future research endeavors may explore alternative predictive models to further assess and compare their performance against the outcomes presented in this study, contributing to the ongoing enhancement of slipper quality prediction methodologies.

COVID-19 Confirmed Cases Forecasting in Malaysia Using Linear Regression and Holt's Winter Algorithm

The 2019 coronavirus disease pandemic (COVID-19)has emerged and is spreading rapidly over the world.Therefore, it may be highly significantto have the general population tested for COVID-19. There has been a rapid surge in the use of machine learning to combat COVID-19 in the past few years, owing to its ability to scale up quickly, its higher processing power, and the fact that it is more trustworthy than peoplein certainmedicaltasks. In this study, we comparedbetweentwo different models: the Holt’s Winter(HW)model and the Linear Regression (LR) model.To obtain the data set of COVID-19, we accessed the website of the Malaysian Ministry of Health.From January 24th, 2020, through July 31st, 2021, daily confirmed instances were documented and saved in Microsoft Excel. Case forecasts for the next 14 days were generated in the Waikato Environment for Knowledge Analysis (WEKA), and the accuracy of the forecasting models was measured by means of the Mean Absolute Percentage Error (MAPE).According to the lowest value of performance indicators, the best model is picked. The results of the comparison demonstrate that Holt's Winter showed betterforecasting outcome than the Linear Regression model. The obtainedresultdepicted the forecasted model can be further analyzed for the purpose of COVID-19 preparation and control.

Using Machine Learning (ML) Models to Predict Risk of Venous Thromboembolism (VTE) Following Spine Surgery.

A retrospective cohort study. Venous thromboembolism (VTE) is a potentially high-risk complication for patients undergoing spine surgery. Although guidelines for assessing VTE risk in this population have been established, development of new techniques that target different aspects of the medical history may prove to be of further utility. The goal of this study was to develop a predictive machine learning (ML) model to identify nontraditional risk factors for predicting VTE in spine surgery patients. A cohort of 63 patients was identified who had undergone spine surgery at a single center from 2015 to 2021. Thirty-one patients had a confirmed VTE, while 32 had no VTE. A total of 113 attributes were defined and collected via chart review. Attribute categories included demographics, medications, labs, past medical history, operative history, and VTE diagnosis. The Waikato Environment for Knowledge Analysis (WEKA) software was used in creating and evaluating the ML models. Six classifier models were tested with 10-fold cross-validation and statistically evaluated using t tests. Comparing the predictive ML models to the control model (ZeroR), all predictive models were significantly better than the control model at predicting VTE risk, based on the 113 attributes ( P <0.001). The Random Forest model had the highest accuracy of 88.89% with a positive predictive value of 93.75%. The Simple Logistic algorithm had an accuracy of 84.13% and defined risk attributes to include calcium and phosphate laboratory values, history of cardiac comorbidity, history of previous VTE, anesthesia time, selective serotonin reuptake inhibitor use, antibiotic use, and antihistamine use. The J48 model had an accuracy of 80.95% and it defined hemoglobin laboratory values, anesthesia time, beta-blocker use, dopamine agonist use, history of cancer, and Medicare use as potential VTE risk factors. Further development of these tools may provide high diagnostic value and may guide chemoprophylaxis treatment in this setting of high-risk patients.

Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire.

This paper presents a novel analytical approach for improving patients' experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include developing a bot to scrap web data while performing sentiment analysis and extracting keywords from National Health Service (NHS) rate and review webpages, building a classifier with Waikato Environment for Knowledge Analysis (WEKA), analyzing speech with Python, and using Microsoft Excel for analysis. In the selected context, a total of 178 reviews were extracted from General Practitioners (GP) websites within Northamptonshire County, UK. Accordingly, 4764 keywords such as "kind", "exactly", "discharged", "long waits", "impolite staff", "worse", "problem", "happy", "late" and "excellent" were selected. In addition, 178 reviews were analyzed to highlight trends and patterns. The classifier model grouped GPs into gold, silver, and bronze categories. The outlined analytical approach complements the current patient feedback analysis approaches by GPs. This paper solely relied upon the feedback available on the NHS' rate and review webpages. The contribution of the paper is to highlight the integration of easily available tools to perform higher level of analysis that provides understanding about patients' experience. The context and tools used in this study for ranking services within the healthcare domain is novel in nature, since it involves extracting useful insights from the provided feedback.

The Use of Clustering and Classification Methods in Machine Learning and Comparison of Some Algorithms of the Methods

In this article, two machine learning methods such as classification and clustering are used for decision tree (DT), artificial neural network (ANN), and K-nearest neighbors algorithms. The datasets were used to evaluate the effectiveness of the clustering method and the data mining tool. Weather data were used to compare algorithms and methods in the study. This study showed that the best model was DT according to accuracy and precision measures but the best model according to F-measure and receiver operating characteristic curve area measures was ANN. Waikato Environment for Knowledge Analysis, a data mining tool, is utilized in this paper to carry out the clustering.

Determinant Football Elements for Euro16 Match Results

Purpose: The current article aimed to verify patterns that could explain match results of the Euro Cup 2016, using as references the following match performance indicators: finishing, ball possession, passing, crosses and defensive actions. Methodology/approach: The necessary information about the matches was collected from the Dailymail’s website. The Weka (Waikato Environment for Knowledge Analysis) program was adopted to help in the analyses of these data. Originality/Relevance: Football matches components have been analysed in order to understand the behavior of the teams in the competitive scenario of this sport. Key findings: The results show that the more accurate shots a national squad takes in their games, the more chances they got to win, instead of using a game plan based on crossing movements. Ball possession, on its turn, can be a good indicator or a bad one to win matches, when associated to a high number of crosses. Moreover, the alternative of long passes seems to be relevant only when the opponent makes more shots from outside the area or get more blocked shots. Complementarily, defensive actions focused on successful tackles and more interceptions appear as a positive aspect for getting the triumph, considering only the total number of tackles. The accuracy of kicks and defensive actions were relevant to the success of the teams in the competition, suggesting that the improvement of actions related to these two indicators should help national teams to improve their own strengths, in order to maximize their chances of winning matches. Theoretical/methodological contributions: Collecting a large volume of football data from a website and using the Weka program have helped not only to speed up and facilitate the analyses but also should have been important in order not to lose any relation that could be relevant inside the studied scenario.

Determinant Football Elements for Euro16 Match Results

Purpose: The current article aimed to verify patterns that could explain match results of the Euro Cup 2016, using as references the following match performance indicators: finishing, ball possession, passing, crosses and defensive actions. Methodology/approach: The necessary information about the matches was collected from the Dailymail’s website. The Weka (Waikato Environment for Knowledge Analysis) program was adopted to help in the analyses of these data. Originality/Relevance: Football matches components have been analysed in order to understand the behavior of the teams in the competitive scenario of this sport. Key findings: The results show that the more accurate shots a national squad takes in their games, the more chances they got to win, instead of using a game plan based on crossing movements. Ball possession, on its turn, can be a good indicator or a bad one to win matches, when associated to a high number of crosses. Moreover, the alternative of long passes seems to be relevant only when the opponent makes more shots from outside the area or get more blocked shots. Complementarily, defensive actions focused on successful tackles and more interceptions appear as a positive aspect for getting the triumph, considering only the total number of tackles. The accuracy of kicks and defensive actions were relevant to the success of the teams in the competition, suggesting that the improvement of actions related to these two indicators should help national teams to improve their own strengths, in order to maximize their chances of winning matches. Theoretical/methodological contributions: Collecting a large volume of football data from a website and using the Weka program have helped not only to speed up and facilitate the analyses but also should have been important in order not to lose any relation that could be relevant inside the studied scenario.