Data Mining Decision Tree Research Articles

ANALISIS PEMELIHARAAN PREDIKTIF KENDARAAN OPERASIONAL MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN DECISION TREE

Utilizing the team to carry out operational vehicle maintenance activities is crucial to maintaining smooth mobility. Well-scheduled maintenance can prevent unexpected problems and minimize disruption to vehicle operations. The problem is that the implementation of operational vehicle maintenance policies is not yet optimal. The aim of this research is to focus on operational vehicles so that use is not disrupted and mobility runs smoothly, maintenance scheduling is needed. Completion of this research method will use the Naïve Bayes and Decision Tree data mining applications. This research produces a comparison of the two data mining applications to determine maintenance performance with an accuracy level of the Naïve Bayes method of 33.33% and a Decision Tree at 75.00%. The results of the best algorithm performance analysis are used as a reference for implementing vehicle maintenance scheduling.

Intelligent financial management system based on Data Mining Technology

This article is based on decision tree algorithm and data mining analysis technology to achieve the intelligence of financial management and decision-making in enterprise, providing effective support for financial management. An improved metric based C4.5 decision tree algorithm was proposed, which combines data warehousing, data mining, and analysis techniques, and adopts a data-driven approach to establish intelligent financial management in enterprise. Apply it to early warning of budget execution progress in financial projects, conduct experimental analysis and result research. In this experiment, data mining achieved significant results. From the root node of the decision tree, it can be seen that whether the project budget execution progress at the end of July is greater than 22.79% is of great significance for determining whether the project budget can reach 97% of the year-end budget execution progress. Based on the experimental results, it is concluded that as of July, more attention should be paid to the execution of project budgets, and effective management of data is the primary task and goal of financial institutions in enterprise.

Research on the Construction of College Students’ Employment and Entrepreneurship Guidance Course System under the Background of “Internet+”

Abstract With the arrival of the “Internet Plus” era, college students are facing new challenges of Employment and entrepreneurship. This study aims to explore an effective guidance course system for Employment and entrepreneurship to meet the current social development needs and enhance students’ entrepreneurial ability and employment competitiveness. Through data mining decision tree algorithm, college students’ employment and entrepreneurship situation and their influencing factors were analyzed to establish an effective guidance course system. It was found that factors such as gender, specialty category, and entrepreneurial experience significantly affect students’ entrepreneurial intention. The results of the decision tree analysis show that factors such as family self-employment situation, entrepreneurial attitude, subjective norms, perceived behavioral control, entrepreneurial self-efficacy and entrepreneurship are closely related to the entrepreneurial intention of college students. Based on this, strategies such as building innovation and entrepreneurship simulation and rehearsal carriers, broadening practical teaching bases, and constructing supportive entrepreneurial entity projects are proposed to promote the enhancement of college students’ entrepreneurial ability and the adaptability of the job market.

PISA 2018 TÜRKİYE MATEMATİK OKURYAZARLIĞINI AÇIKLAYAN DEĞİŞKENLERİN CHAID ANALİZİ İLE İNCELENMESİ

This study aims to investigate the variables explaining mathematics literacy of Turkish students who attended at Programme for International Student Assessment (PISA). The topic of the study is at utmost importance due to the potential findings that will identify the variables influencing mathematical literacy in our education system and generate recommendations aimed at addressing shortcomings.This study utilizes the answers given to the PISA student questionnaire, which are analysed by correlational survey design.Stratified sampling design is used in the selection of 6890 students in Turkish sample. After excluding the missing data, the sample of the study consists of 5293 participants. Chi-squared Automatic Interaction Detection (CHAID) method, which is one of the data mining decision tree algorithms, is used for data analysis. According to the results of the study, the most important variable explaining Turkish students’ mathematic literacy is the number of the books at home. Father’s education level, highest parental education level, accessibility of ICT both at home and school, and time allocated per week to study mathematics are other variables explaining mathematics literacy.

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

Abstract This paper utilizes the data mining decision tree ID3 algorithm to improve the traditional ID3 algorithm, exclude the influence of other factors, and realize the decision tree ID3 algorithm by using the data mining decision tree ID3 algorithm in the database of college students’ mental health assessment in the application of mental health assessment in colleges and universities as an example. Among them, the number of nodes, the number of rules, the classification accuracy and the time of constructing the decision tree of the algorithm are compared to verify the improvement effect of the ID3 algorithm. The target dataset consists of psychological assessment data of students, which includes their basic situation and nine-dimensional psychological symptoms. Analyze the recorded data of students’ mental health status, extract the information on personality, parental relationship, economic income, and psychological abnormality, set the decision tree analysis variables, assign the value of students’ health status, and derive the specific factors affecting students’ mental health by using the decision tree If-Then classification rules. The validation results show that the generated decision tree ID3 model cross-validation estimate is 0.261, the standard error is 0.016, and its obtained standard error is less than 0.018, which indicates that the model fits better.

Health Information Management and Patient Profile Estimation Using Data Mining Classification Techniques

Nowadays, the rapid advancement of technological developments consists of complex and large databases. Software programmes are needed because it can be very difficult to process the complex data. The aim of the study is to reveal the patient profile by data mining of the hospital information system and to help make strategic decisions according to patient profile analysis in the establishment of a new hospital or additional hospital departments in the future. The application of data mining in the health sector provides the sector with a faster, reliable and different perspective on decision-making by reaching the desired data. For this purpose, data mining decision tree algorithms were applied, and patient profile estimations were found by using the data of patients receiving service from a private health institution with hospitals in different settlements. By categorising the characteristics of the individuals receiving service and the branches from which they receive service, it was revealed with which variables the patient profile is related. It is thought that determining patient profile will help to follow a more accurate path in the stage of meeting their demands.

Market Analysis for Farmers using Machine Learning

Using big data Data mining and predictive analysis have developed to be helpful for examining crop prices in agriculture. This paper considers applying methods of Big Data and data mining utilising agricultural data. As a result of an outdated chain structure for selling the crops in their various markets, farmers are increasingly concerned with getting the full worth of the commodity. Due to the fact that farmers in the area may only sell their goods in a way that indirectly affects consumers, the market's true potential is still untapped. E-agriculture is a growing industry that focuses on improving farming and advancing provincial development through better data and correspondence forms. The market has a growing amount of data, just as other industries. Data from the various markets in the Bangalore area are taken into account and employed in this work's data mining decision tree models. Farmers are the backbone of our nation, which is dealing with a number of issues. Among these issues, getting pertinent information about the crop and weather specifics at the appropriate moment is one of the most significant. The primary objective of the Map-Reduce-based decision tree model is to give Farmers the greatest price for their product. In comparison to other models, the output of the proposed Map Reduce model is more accurate at predicting the best price from the existing market data.

Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost)

The Decision Tree algorithm is a data mining method algorithm that is often applied as a solution to a problem for a classification. The Decision Tree C5.0 algorithm has several weaknesses, including: the C5.0 algorithm and several other decision tree methods are often biased towards modeling whose features have many levels, some problems for the model can occur such as over-fit or under-fit challenges, big changes to decision logic can result in small changes to data training, C5.0 can experience modeling inconvenience, data imbalance causes low accuracy in C5.0 algorithm. The boosting algorithm is an iterative algorithm that gives different weights to the distribution of training data in each iteration. Each iteration of boosting adds weight to examples of misclassification and decreases weight to examples of correct classification, thereby effectively changing the distribution of the training data. One example of a boosting algorithm is adaboost. The purpose of this research is to improve the performance of the Decision Tree C5.0 classification method using adaptive boosting (adaboost) to predict hepatitis disease using the Confusion matrix. Tests that have been carried out with the Confusion Matrix use the Hepatitis dataset in the Decision Tree C5.0 classification which has an accuracy rate of 80.58% with a classification error rate of 19.15%. Whereas in the Decision Tree C5.0 classification Adaboost has a higher accuracy rate of 82.98%, a classification error rate of 17.02%. This difference is caused by the adaboost algorithm, because the adaboost algorithm is able to change a weak classifier into a strong classifier by increasing the weight of the observations, and adaboost is also able to reduce the classifier error rate.

Application of C4.5 Classification in Improving Recitation Fluency in Students

Fluency in reciting the Koran is learning the recitation of the Qur'an in a tartil way. Based on the observations of researchers, the learning of tajwid in recitation and at school has not been effective so far. Because of this, both teachers and students at recitation or at school need improvement by finding out what can increase fluency in reciting the Koran and what has the most influence on improving fluency. So this study aims to improve the fluency of the Koran in students. The research method used is the Decision Tree data mining classification method with the C4.5 algorithm. The results of data processing with the C4.5 algorithm using the Rapidminer tools are attribute C1(fluency) being the most influential attribute for increasing students' reading fluency and performance data obtained with an accuracy of 83,33%.

Optimization of the Decision Tree Method using Pruning on Liver Disease Classification

The amount of data about liver disease can be used to become information that can be extracted using the decision tree data mining method. However, there is a weakness in the decision tree method, namely over-fitting the resulting tree can produce a good model in training data but normally cannot produce a good tree model when applied to unseen data. Based on experiments conducted using datasets taken from The UCI Machine Learning Repository database is the ILPD dataset which contains 583 clinical data with 10 attributes with a target output of 416 positive liver and 167 negative liver. The results show that the decision tree algorithm using pruning and without pruning has been tested showing an increase in accuracy. The results of the decision tree performance without pruning generated in the confusion matrix for the accuracy measure, which is 73.58 %. While the results of the system performance using the pruning method have an accuracy of 73.76%. Although the accuracy value is slightly adrift, it can prove that the decision tree method using the pruning method has much better accuracy. In addition, the models and rules generated by the decision tree can be used as the basis for developing a prototype application for liver disease classification.

An Auto-Extraction Framework for CEP Rules Based on the Two-Layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting

Energy is at the center of human society and drives the technologies and overall human well-being. Today, artificial intelligence (AI) technologies are widely used for system modeling, prediction, control, and optimization in the energy sector. The internet of things (IoT) is the core of the third wave of the information industry revolution and AI. In the energy sector, tens of billions of IoT appliances are linked to the Internet, and these appliances generate massive amounts of data every day. Extracting useful information from the massive amount of data will be a very meaningful thing. Complex event processing (CEP) is a stream-based technique that can extract beneficial information from real-time data through pre-establishing pattern rules. The formulation of pattern rules requires strong domain expertise. Therefore, at present, the pattern rules of CEP still need to be manually formulated by domain experts. However, in the face of complex, massive amounts of IoT data, manually setting rules will be a very difficult task. To address the issue, this paper proposes a CEP rule auto-extraction framework by combining deep learning methods with data mining algorithms. The framework can automatically extract pattern rules from unlabeled air pollution data. The deep learning model we presented is a two-layer LSTM (long short-term memory) with an attention mechanism. The framework has two phases: in the first phase, the anomalous data is filtered out and labeled from the IoT data through the deep learning model we proposed, and then the pattern rules are mined from the labeled data through the decision tree data mining algorithm in the second phase. We compare other deep learning models to evaluate the feasibility of the framework. In addition, in the rule extraction stage, we use a decision tree data mining algorithm, which can achieve high accuracy. Experiments have shown that the framework we proposed can effectively extract meaningful and accurate CEP rules. The research work in this paper will help support the advancement of the sector of air pollution prediction, assist in the establishment of air pollution regulatory strategies, and further contribute to the development of a green energy structure.

Prediction Model of Production Completion Delay to Improve Service Quality Using Decision Tree Versus Multilayer Perceptron Method

Delays in the completion of pvd production can be caused by several factors. Including the actual experience in the production of the difficulty of each process and color type, even the difficulty of the product type can also be affected. In this study, the prediction of the delay in the completion of pvd production was carried out using the C4.5 decision tree and Multilayer Perceptron data mining method approach using Production Results data at PT. Surya Toto Indonesia, whose results are expected to provide information and input for the company in making production plans in the future. The data testing method was carried out with 5 (five) testing times with different amounts of data to determine the level of consistency of accuracy obtained. C4.5 gives the results of a decision tree where the root is the color type and as the leaf is the product category, type type and order period. The average value of accuracy generated in the C4.5 decision tree method is 87.15%. While the Multilayer Perceptron obtained an average accuracy of 87.98%, which is greater than the decision tree method with a difference of 0.83%.

Investigation of Variables Explaining Science Literacy in PISA 2015 Turkey Sample

The purpose of this study was to examine the variables that explain science literacy with the answers given by Turkish students to the Program for International Student Assessment (PISA) 2015 student questionnaire. The Turkish sample of the research, which was conducted in a relational scanning model, is composed of 5895 students selected through a stratified sampling design. The sample of the study consists of 3052 people who remained after the data containing missing values were removed. In this study, Chi-squared Automatic Interaction Detection (CHAID) method, which is one of the data mining decision tree algorithms, was used for data analysis. As a result of the analysis, it was concluded that the variable that best explains the science literacy of Turkish students is “the number of books in the home”. Other variables explaining the science literacy of Turkish students were also investigated in detail. In the research, gain values were examined to determine the most effective node in separating successful and unsuccessful students. As a result, it was seen that the most effective node consisted of students with science self-efficacy among students who had more than 200 books at home, and those who had more than 40 course hours in a week at school.

Retraction Note to: A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

Retraction Note to: A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

Feature Extraction of National Physical Fitness Data Based on Data Mining

In order to better understand and optimize the national physical fitness, this paper puts forward the national physical fitness data change feature extraction method based on data mining, uses the decision tree and association rule data mining algorithm to collect the national physical fitness data in recent years, constructs the database to realize the effective data management, and uses the data mining algorithm to construct the physical fitness change feature evaluation index. Finally, through experiments, it is confirmed that the national physique data change feature extraction method based on data mining has high effectiveness in the process of practical application. It can better understand the national physique change trend and put forward targeted suggestions for national physique health optimization.

Прогностические маркеры исхода острого периода инфаркта миокарда

Исход острого периода инфаркта миокарда прогнозировался на основании статистического метода с применением алгоритма Data Mining «дерево решений». В качестве входных данных использовались чувствительные и специфические векторкардиографические показатели. Установлены прогностически значимые маркеры исхода острого периода инфаркта миокарда. Если возраст больного был 57 лет и более, а скорость распространения возбуждения по миокарду высоких отделов передней стенки левого желудочка замедлялась до 6,33 мв/с, прогнозировался летальный исход с точностью 92,86 %. В случае регистрации у пациента увеличения площади предсердных петель более 0,188 мм2 (в ВА2) с угловым расхождением петель QRS-P более 15,5° (в ВА1), размыкания петли QRS менее 2,834 мм (в 3-й проекции) и скорости распространения возбуждения в конечной части петли Т менее 2,338 мв/с (в ВА1) при различных значениях углового расхождения вектора QRS-Т (в ВА5) прогнозировался благоприятный исход с точностью 97,67 %.

Rectal cancer response to neoadjuvant chemoradiotherapy evaluated with MRI: Development and validation of a classification algorithm

ObjectiveThe aim of this study was to develop and validate a decision support model using data mining algorithms, based on morphologic features derived from MRI images, to discriminate between complete responders (CR) and non-complete responders (NCR) patients after neoadjuvant chemoradiotherapy (CRT), in a population of patients with locally advanced rectal cancer (LARC). MethodsTwo populations were retrospectively enrolled: group A (65 patients) was used to train a data mining decision tree algorithm whereas group B (30 patients) was used to validate it. All patients underwent surgery; according to the histology evaluation, patients were divided in CR and NCR. Staging and restaging MRI examinations were retrospectively analysed and seven parameters were considered for data mining classification.Five different classification methods were tested and evaluated in terms of sensitivity, specificity, accuracy and AUC in order to identify the classification model able to achieve the best performance.The best classification algorithm was subsequently applied to group B for validation: sensitivity, specificity, positive and negative predictive value, accuracy and ROC curve were calculated. Inter and intra-reader agreement were calculated. ResultsFour features were selected for the development of the classification algorithm: MRI tumor regression grade (MR-TRG), staging volume (SV), tumor volume reduction rate (TVRR) and signal intensity reduction rate (SIRR). The decision tree J48 showed the highest efficiency: when applied to group B, all the CR and 18/21 NCR were correctly classified (sensitivity 85.71%, specificity 100%, PPV 100%, NPV 94.2%, accuracy 95.7%, AUC 0.833).Both inter- and intra-reader evaluation showed good agreement (κ > 0.6). ConclusionsThe proposed decision support model may help in distinguishing between CR and NCR patients with LARC after CRT.

CNS: Interactive Intelligent Analysis of Financial Management Software Based on Apriori Data Mining Algorithm

With the steady development of information technology, efficient, fast and safe enterprise financial management has received the focused attention of enterprise managers. Not only that, the integration of enterprise departments and financial operations is getting closer and closer. With the increasing business volume of enterprises, the interactive data generated by the data management system of the financial departments of enterprises has become increasingly prominent. Based on the research and analysis of enterprise financial data, combined with Apriori, clustering, decision tree and other data mining technologies, this paper extracts and utilizes massive financial data generated by the enterprise financial department in the daily management process, and designs and implements an intelligent analysis system of enterprise financial data based on data mining. It provides helpful financial information for the financial management department and senior management and assists the enterprise in making quick decisions.

Data Mining Technology Used in an Internet of Things-Based Decision Support System for Information Processing Intelligent Manufacturing

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance.

University Classroom Teaching Model Based on Decision Tree Analysis and Machine Learning

The existing teaching evaluation system partially reflects the teaching effect and other related conditions through statistical reports, but it is difficult to find the useful knowledge hidden in the database, and it cannot effectively assist the decision-making support. In order to improve the evaluation effect of college classroom teaching mode, this paper mainly uses decision tree algorithm and data mining technology of association rules to construct the effectiveness evaluation system of college classroom teaching mode based on decision tree analysis. Moreover, this paper analyzes the teaching information and evaluation data to extract the potentially useful knowledge contained in it, which can help decision-makers find the rules and explore various factors that affect the teaching effect of teachers, thereby improving teaching management and optimizing resource allocation. In addition, this paper uses experimental teaching methods to verify the performance of the system model constructed in this paper. The research results show that the system constructed in this paper has certain reliability and practicability.