Application Of Data Mining Techniques Research Articles

Application of data mining techniques for early detection of heart diseases using Framingham heart study dataset

Application of data mining techniques for early detection of heart diseases using Framingham heart study dataset

HIGHER EDUCATION DASHBOARD IMPLEMENTATION USING DATA MINING AND DATA WAREHOUSE: A REVIEW PAPER

Most of the activities including lecturer recruiting, advertising, pupil admittance, scholar registration, educational procedures, and alumni system, most higher education institutions now use integrated information systems. The higher education information system produces a lot of transactional information, and the quantity of data is growing every day, nonetheless the best way to utilize it is yet unknown. Each piece of data saved on a data storage medium is used for a certain purpose. A higher education institution, on the other hand, requires a comprehensive understanding of all data. In light of these circumstances, an analytical tool is required to excerpt statistics and uncover useful knowledge from large amounts of data. Such procedures necessitate a longer processing time and a more complicated procedure. The goal of this study was to create a data warehouse model and dashboard for an analytical tool, as well as to apply data mining techniques to higher education institutions. The study approach started with the creation of knowledge requirements, followed by the creation of an evolutional archetype to a data warehouse, the application of data mining methods, and the creation of a console for an investigative utilities. The end consequence was basically a prototype of data mining, data warehouse, and systematic utility for higher education institutions that would help them enhance their analytical and decision-making processes and therefore improve their performance.

Analysis and Prediction of Students’ Academic Performance and Employability Using Data Mining Techniques: A Research Travelogue

Higher education institutions (HEIs) handle tons of data to analyze and generate the most relevant information. Data mining is considered a useful tool to extract knowledge to predict future educational trends in this process. Hence, such a method is significant to HEIs to understand and predict students’ employability and other critical academic elements. A comprehensive and systematic literature review was conducted to identify data mining techniques, algorithms, and the various data sets that will lead to the smart prediction and accuracy of student employability. The same method was used to determine the relationship between academic achievement and the employability of students. According to the research findings, the most frequently used data mining techniques for determining students' academic achievement and employability are Classification techniques, specifically the J48(C4.5) algorithm, the Naïve Bayes algorithm, and the CHAID Decision Tree algorithm. The most frequently used data sets or attributes for predicting students' academic performance and employability are their cumulative grade point average (CGPA), gender, technical, communication, problem-solving, analytical, critical thinking, and decision-making skills, extracurricular activities, and age, as well as psychomotor factors such as behavior and attendance and training/internship placement. Academic performance is the primary determinant of employability. The application of data mining techniques in academia has demonstrated its value in enhancing the performance of higher education institutions (HEIs). As a result, more research is urgently needed to ascertain the efficacy of the approaches, algorithms, and data sets identified as predictors of students' employability. Moreover, automated approaches should be utilized to ascertain their accuracy.

Customerchurnprediction fortelecommunicationindustry: A Malaysian Case Study.

Background:Customer churn is a term that refers to the rate at which customers leavethebusiness.Churncould be due tovariousfactors, including switching to a competitor, cancelling their subscriptionbecause ofpoor customer service, or discontinuing all contact with a brand due to insufficient touchpoints.Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companiescananticipate future revenue. Thisarticlewill look at which variables in theNet Promoter Score (NPS)dataset influence customer churn in Malaysia's telecommunications industry. The aim of This study wastoidentify the factorsbehind customer churn andpropose achurn predictionframeworkcurrently lackinginthe telecommunications industry. Methods:This study applied data mining techniques totheNPSdataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with30 fields to determine which variableswere significant for the churn prediction model.We developed a propensity forcustomer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees(CART), Gaussian Naïve Bayes, and Support Vector Machineusing 33variables. Results:Customer churn is elevated for customers with a lowNPS.However, an immediate helpdeskcanact as a neutral partyto ensure that thecustomer needs are met andtodeterminean employee's abilityto obtain customer satisfaction. Conclusions:It can be concludedthat CART has the most accuratechurn prediction(98%).However,theresearchis prohibited from accessing personal customer information under Malaysia's data protection policy.Resultsare expected for other businessesto measurepotential customerchurnusingNPS scores to gather customer feedback.

CLASSIFICATION OF STUDENT SATISFACTION WITH ONLINE LECTURE

Abstra Covid-19 has had a significant impact on people's lives, resulting in the paralysis of almost the entire economy and education, especially in the education sector, resulting in many students being unable to carry out teaching and learning activities at schools or universities. Based on this, the Ministry of Education and Culture has issued an appeal to stop face-to-face teaching and learning activities at schools and universities and replace them with distance or online learning. Resulting in teaching and learning activities to be less than optimal for students or students, there is dissatisfaction with the distance or online learning system, the purpose of this study is to measure the level of student satisfaction with online lectures by applying data mining techniques, classifying the level of online learning satisfaction using an online learning approach. k-NN algorithm and Decision Tree with 100 questionnaire data that has been collected from active students who carry out online lectures with an accuracy rate of 96.00% from the k-NN algorithm and a satisfied precision value of 95.51%, a satisfied recall value of 98.84% on a precision value the dissatisfied class is 90.91%, the recall value of the dissatisfied class is 71.43%. While the accuracy results using the Decision Tree algorithm approach is lower with an accuracy of 95.00%. based on research results that the level of student satisfaction with distance learning or online is quite high.
 Keywords: covid 19, data mining, online, k-NN, decision tree

A data mining approach for automatic classification of rock permeability

Reliable estimates of porosity can be obtained from different types of geophysical well logs. However, obtaining in situ permeability estimates is still a major challenge in the geosciences. This work aims to evaluate the application of data mining techniques to NMR logs for rock permeability classification, thus far tested only on laboratory data. For this study, we used a petrophysical database from two Brazilian Pre-salt wells located in the Santos Basin, a formation notoriously difficult to characterize, mainly due to its diversity and complexity. Six classification algorithms were evaluated (k-NN, NB, C4.5, RF, SMO, and MLP) according to their ability to estimate the permeability of rocks in four distinct classes (low: <1 mD, intermediate: 1–10 mD, high: 10–100 mD, and excellent: >100 mD). The predictive performance of the algorithms was compared to the behavior of two traditional permeability estimators. With an accuracy of 66%, the Naïve Bayes algorithm, combined with two preprocessing steps – unsupervised discretization and attribute selection – achieved the highest predictive performance. That mark surpassed the accuracy obtained by Kenyon and Timur-Coates estimators by 154% and 106%, respectively, providing evidence for the superiority of the data mining technique to recognize permeability classes based on NMR logs. Classification experiments employing NMR logs in conjunction with conventional logs were also conducted, but this log combination was not able to best the predictive result based solely on the NMR log data.

A Review of Predictive Systems for Patients at Risk of Developing Stroke

Stroke remains one of the leading causes of death worldwide. It is usually associated with a build-up of fatty deposits inside the arteries which increases the risk of blood clotting. The unannounced nature of the disease when it strikes has posed a major challenge in the health sector. Poor medical facilities, insufficient information on how to accurately diagnose stroke, late identification of the disease by the patients due to being ignorant of the disease are some of the reasons for the increasing mortality rate due it. The application of data mining technique in the field of medicine has brought about positive development in the area of diagnosing, prediction and deeply understanding of healthcare data. This study considers some of the Predictive Models developed using some data mining approaches to predict patients at risk of developing stroke in order for other researchers to build on.

Applying data mining techniques to higher-dimensional Poincaré maps in the circular restricted three-body problem

Applying data mining techniques to higher-dimensional Poincaré maps in the circular restricted three-body problem

Analysis of Students’ Behavior in English Online Education Based on Data Mining

With the formation of global economic integration for better exchange and cooperation with nations around the world, mastering English is extremely essential. In the context of today’s big era with a variety of English learning methods, it is required that data mining be applied to online English education. Owing to the continuous application of data mining techniques and the improvement of the online learning system, its application in education is also more and more prevalent. In the face of a large amount of learning data and student behavior data, the traditional methods have the problems of low processing efficiency, more memory requirements, and large prediction error. Therefore, this paper proposes a student behavior analysis method of online English education based on data mining. The student behavior data is collected, and an online English education learning behavior model is established. The data mining model is built to filter the obtained behavior data through data preparation, data statistics, and analysis. Furthermore, the apriori algorithm is used to mine association rules and calculate the similarity of data followed by the application of a fuzzy neural network to mine the behavior data of English online education students. The experimental results show that this method has high data processing efficiency, takes up less space, and produces a low prediction error.

Evaluating evapotranspiration using data mining instead of physical-based model in remote sensing

Precise calculations for determining the water requirements of plants and the extent of evapotranspiration are crucial in determining the volume of water consumed for plant production. In order to estimate evapotranspiration over an extended area, different remote sensing algorithms require numerous climatological variables; however, climatological variable measurements cover only limited areas thus resulting into erroneous calculations over extended areas. The exploiting of both data mining and remote sensing technologies allows for the modeling of the evapotranspiration process. In this research, the physical-based SEBAL evapotranspiration algorithm was remodeled using M5 decision tree equations in GIS. The input variables of the M5 decision tree consisted of Albedo, emissivity, and Normalized Difference Water Index (NDWI) which were defined as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS using python scripts for 8 April 2019 and 3 April 2020, respectively. The calculated correlation coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42, respectively, and for 3 April 2020 were 0.95, 0.31, and 0.23 in order. Moreover, for the further evaluation of the model, a sensitivity analysis and an uncertainty analysis were carried out. The analysis revealed that evapotranspiration is more sensitive to Albedo than the two other model inputs, and when applying data mining techniques instead of SEBAL, the estimation of evapotranspiration has a lower accuracy.

Applying data mining techniques to explore technology adoptions, grades and costs of green building projects

Many architects encounter problems during adoption of green building technologies and are unfamiliar with the benefits of green buildings. This study conducted two-stage data mining on 354 green building projects in Taiwan, in order to solve issues in the preliminary design phase of projects, such as technology adoptions, green building grades, and construction costs. This study adopted the association rules to explore the associations between different types and grades of green buildings and technology adoptions. Moreover, a prediction model based on the artificial neural network was constructed to predict the grades and costs of green building projects. The results indicate that different types and grades of green buildings are based on varying green building technologies. In particular, a green building with a high grade places more emphases on the green building technologies of air conditioning, CO2 reduction, and indoor environments. The green building technologies are affected by building types, regulations, costs, climate conditions, or geographic restrictions. This study also found that the accuracy of the artificial neural network in predicting green building grades and costs can reach above 80%. The systematic data mining method constructed herein can effectively assist architects and building owners to reduce preliminary design time and costs, and improve the success rates of green building projects. It is expected that the proposed approach can be adjusted in the future for other regions with different climates or their corresponding green building rating tools, so as to construct more suitable applications.

Use of Data Warehouse and Data Mining for Academic Data : A Case Study at a National University

If an organisation has full, rapid, exact, and accurate information, it may perform better in terms of evaluation, planning, and decision-making. The essential information can be derived from operational data maintained in an integrated database by a university that already has an information system. This research looks at how to collect operational data into a data warehouse and then apply data mining techniques to analyse the data. This study yielded a comprehensive data warehouse with a web-based information reporting application. Furthermore, data mining techniques are used to analyse the data warehouse that has been created. The result of the application of data mining is the generation of characteristic patterns of students who take certain specialization programs.

A proposed strategy to secure web usage

With much data on the web, it can be difficult, frustrating, and seemingly impossible to find the exact information you need. There are many powerful search utilities on the web are called search engines, in addition to the visitor tracking in a web to study exactly the behavior of the web visitors, to improve the efficiency of that web. This research concentrates on a particular aspect, which is applying Data mining technique especially by association analysis algorithm on the encrypted web log files, that to ensure the privacy of the original data for these files. So since the input data introduced to mining algorithm is encrypted then the resulted association rules are encrypted that to ensure the privacy of the extracted knowledge. Then analyze the decrypted web log data file for the web usage, to study the visitor tracking. According to this study the server configurations and all the services will be improved.

Movie Success Prediction Using Data Mining

The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.

Telemarketing Guidance in Selling Banking Services: A Data Mining Approach

In telemarketing activity, selecting the most potential customers are important because can reduce processing time and operational cost. Therefore, the ability to select the most likely buying customers are urgently needed. In this study, we propose a clear sequence in doing telemarketing activity based on the previous telemarketing data which applying data mining technique. We weight the importance of 16 customer characteristics through 45,211 observations from a Portuguese bank. Applying Random Forest algorithm along with Information Gain Ratio as a criterion and 10-fold Cross Validation, the model able to weight the importance of attributes and achieves 90.01 % accuracy in predicting telemarketing success. Furthermore, the rank of attribute importance was designed to be a guidance map in selecting potential targeted customers as a managerial implication.

Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study.

BackgroundUnscheduled emergency department return visits (EDRVs) are key indicators for monitoring the quality of emergency medical care. A high return rate implies that the medical services provided by the emergency department (ED) failed to achieve the expected results of accurate diagnosis and effective treatment. Older adults are more susceptible to diseases and comorbidities than younger adults, and they exhibit unique and complex clinical characteristics that increase the difficulty of clinical diagnosis and treatment. Older adults also use more emergency medical resources than people in other age groups. Many studies have reviewed the causes of EDRVs among general ED patients; however, few have focused on older adults, although this is the age group with the highest rate of EDRVs.ObjectiveThis aim of this study is to establish a model for predicting unscheduled EDRVs within a 72-hour period among patients aged 65 years and older. In addition, we aim to investigate the effects of the influencing factors on their unscheduled EDRVs.MethodsWe used stratified and randomized data from Taiwan’s National Health Insurance Research Database and applied data mining techniques to construct a prediction model consisting of patient, disease, hospital, and physician characteristics. Records of ED visits by patients aged 65 years and older from 1996 to 2010 in the National Health Insurance Research Database were selected, and the final sample size was 49,252 records.ResultsThe decision tree of the prediction model achieved an acceptable overall accuracy of 76.80%. Economic status, chronic illness, and length of stay in the ED were the top three variables influencing unscheduled EDRVs. Those who stayed in the ED overnight or longer on their first visit were less likely to return. This study confirms the results of prior studies, which found that economically underprivileged older adults with chronic illness and comorbidities were more likely to return to the ED.ConclusionsMedical institutions can use our prediction model as a reference to improve medical management and clinical services by understanding the reasons for 72-hour unscheduled EDRVs in older adult patients. A possible solution is to create mechanisms that incorporate our prediction model and develop a support system with customized medical education for older patients and their family members before discharge. Meanwhile, a reasonably longer length of stay in the ED may help evaluate treatments and guide prognosis for older adult patients, and it may further reduce the rate of their unscheduled EDRVs.

Traditional versus Microsphere Embolization for Hepatocellular Carcinoma: An Effectiveness Evaluation Using Data Mining.

Background: For hepatocellular carcinoma (“HCC”), the current standard of treatment is hepatic artery embolization, generally through trans-catheter arterial chemoembolization (“TACE”). There are two types: traditional (“conventional” or “cTACE”) and microsphere (“DC bead TACE”). Unfortunately, the literature comparing the relative effectiveness of cTACE versus DC bead TACE is inconclusive, partially due to the complexity of HCC and its response to treatment. Data mining is an excellent method to extract meaning from complex data sets. Purpose: Through the application of data mining techniques, to compare the relative effectiveness of cTACE and DC bead TACE using a large patient database and to use said comparison to establish usable guidelines for developing treatment plans for HCC patients. Materials and Methods: The data of 372 HCC patients who underwent TACE in Taichung Veterans General Hospital were analyzed. The chi-square test was used to compare the difference in the effectiveness of the two therapies was compared. Logistic regression was used to calculate the odds ratios. Furthermore, using the C4.5 decision tree, the two therapies were classified into applicable fields. Chi-square test, the t-test, and logistic regression were used to verify the classification results. Results: In Barcelona Clinic Stages A and B cancers, cTACE was found to be 22.7% more effective than DC bead TACE. By using the decision tree C4.5 as a classifier, the effectiveness of either treatment for small tumors was 8.475 times than that for large tumors. DC bead TACE was 3.39 times more successful in treating patients with a single tumor than with multiple tumors. For patients with a single tumor, the chi-square test showed that 100–300 μm microspheres were significantly more effective than 300–500 μm. While these findings provide a reference for the selection of an appropriate TACE approach, we noted that overall accuracy was somewhat low, possibly due to the limited population. Conclusions: We found that data mining could be applied to develop clear guidelines for physician and researcher use in the case of complex pathologies such as HCC. However, some of our results contradicted those elsewhere in the literature, possibly due to a relatively small sample size. Significantly larger data sets with appropriate levels of granularity could produce more accurate results.

Applying data mining techniques for technology prediction in new energy vehicle: a case study in China.

Technology prediction is an important technique to help new energy vehicle (NEV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative technology prediction method to effectively identify core and frontier technologies for NEV firms. Among the various methods of technology prediction, one of the most frequently used methods is to make technology prediction from patent data. This paper synthesizes the frequent pattern growth(FP-growth) algorithm and input-output analysis to construct a new technology prediction method based on the knowledge flow perspective, takes the data of NEV patent family in 1989-2018 the Derwent patent database as a sample, divides the data according to the 5-year standard, and uses the method to identify the core and frontier technologies in the NEV field during different periods. Furthermore, the multiple co-occurrence method applies to analyze the technology layout and evolution patterns in China's NEV field. The results show that the technology prediction method proposed in this paper can effectively identify core and frontier technologies to achieve NEV technology prediction.

Exploring the Mechanism of Scutellaria baicalensis Georgi Efficacy against Oral Squamous Cell Carcinoma Based on Network Pharmacology and Molecular Docking Analysis.

Background Scutellaria baicalensis Georgi (SBG) has been widely shown to induce apoptosis and inhibit invasion and migration of various cancer cells. Increased evidence shows that SBG may be useful to treat oral squamous cell carcinoma (OSCC). However, the biological activity and possible mechanisms of SBG in the treatment of OSCC have not been fully elucidated. This study aimed to clarify the bioactive component and multitarget mechanisms of SBG against OSCC using network pharmacology and molecular docking. Methods Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to predict the active components in SBG, and putative molecular targets of SBG were identified using the Swiss Target Prediction database. OSCC-related targets were screened by GeneCards, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). Then, we established protein-protein interaction (PPI), compound-target-disease (C-T-D), and compound-target-pathway (C-T-P) networks by Cytoscape to identify the main components, core targets, and pharmacological pathways of SBG against OSCC via applying data mining techniques and topological parameters. Metascape database was utilized for Gene Ontology (GO) and pathway enrichment analysis. The potential interaction of the main components with core targets was revealed by molecular docking simulation, and for the correlation between core targets and OSCC prognosis analysis, the Kaplan–Meier Plotter online database was used. Results There were 25 active compounds in SBG and 86 genes targeted by OSCC. A total of 141 signaling pathways were identified, and it was found that the PI3K-Akt signaling pathway may occupy core status in the anti-OSCC system. GO analysis revealed that the primary biological processes were related to apoptosis, proliferation, and migration. Molecular docking results confirmed that core targets of OSCC had a high affinity with the main compounds of SBG. Conclusion Our study demonstrated multicomponent, multitarget, and multipathway characteristics of SBG in the treatment of OSCC and provided a foundation for further drug development research.

Applying data mining technique to predict trends in air pollution in Mumbai

Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on which they can take precautionary measures and can protect their health. The problem arises when the level of SO2, NO2 and residual suspended particulate matters in the air increases than that of theirs restricted level. In this paper, the Prophet Algorithm, open source software, is applied to predict the trend of air pollution in the city of Mumbai, Maharashtra. The Prophet is machine learning algorithm to forecast and also to predict time series data. It is based on additive model where non-linear trends are fit with yearly and weekly seasonality. The graphical results are generated after using this algorithm which shows the trending pattern of the pollutants in the air of Mumbai.