Application Of Data Mining Techniques Research Articles

Application of Data Mining Techniques in Healthcare: Identifying Inter-Disease Relationships through Association Rule Mining

This research focuses on the application of data mining techniques in a healthcare environment by utilizing patient visit data from Hospital X, coded with ICD-10 diagnoses. The purpose of this study is to explore the application of data mining techniques in a healthcare environment, specifically to identify the relationship between diseases using patient visit data from X Hospital. This research utilizes the FP-Growth algorithm method followed by Association Rule Mining to find frequent occurrences of diseases in the data set. The research process involved data pre-processing, transformation into binary format, and careful parameter setting (minimum support 0.95 and confidence 0.9). The results showed a strong association between chronic conditions such as hypertension and diabetes, which are prevalent in the patient population. This association provides insight into potential comorbidities and may assist healthcare providers in improving diagnosis accuracy and treatment effectiveness. This research has implications for the application of data mining techniques, demonstrating its potential in improving predictive analytics in healthcare and strategic planning. This approach not only aids in the efficient allocation of healthcare resources, but also aligns with the broader goal of improving personalized patient care.

Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method

This student graduation rate research focuses on analyzing academic performance with the main aim of identifying and distinguishing between students who graduate on time and those who graduate late. The application of data mining techniques in this research uses the neural network method, which is expected to offer deeper insight into the factors that influence students' graduation times. In this study, the neural network method was used to classify graduation data from 150 students. The results of this analysis were very encouraging, with 149 students identified as graduating on time and one student graduating late. The level of accuracy achieved in this classification is 98%, which shows the effectiveness of the neural network method in processing and analyzing academic data. These results confirm that neural networks are a powerful and reliable tool for predictive tasks like this. The successful use of neural networks in this study also proves their potential in broader educational applications, particularly in optimizing educational and intervention strategies. By understanding the characteristics of students who graduate on time versus those who graduate late, educators and administrators can design more effective programs to support student success. This is important not only to improve graduation statistics, but also to improve the overall educational experience for students.

Locating Electrified Aircraft Service to Reduce Urban Congestion

The relentless expansion of urban populations and the surge in e-commerce have increased the demand for rapid delivery services, leading to an increase in truck traffic that contributes to urban congestion, environmental pollution, and economic inefficiencies. The critical challenge this poses is not only in managing urban spaces efficiently but also in aligning with global sustainability goals. This study addresses the pressing need for innovative solutions to reduce reliance on truck transportation in congested urban areas without compromising the efficiency of freight delivery systems. This study contributes a novel approach that leverages electrified and autonomous aircraft (EAA) cargo shuttles to shift the bulk of air transportable freight from road to air, specifically targeting underutilized airports and establishing vertiports in remote locations. By applying data mining techniques to analyze freight flow data, this research identifies key commodity categories and metropolitan statistical areas (MSAs) where the implementation of EAA services could significantly mitigate truck-induced congestion. The findings reveal that targeting a select few commodities and MSAs can potentially decrease truck traffic, with electronics emerging as the dominant commodity category, and cities like Los Angeles and Chicago as prime candidates for initial EAA service deployment. Stakeholders in urban planning, transportation logistics, and environmental policy will find this study’s insights beneficial. This work lays a foundation for future innovations in sustainable urban mobility and logistics.

A Study of Role and Applicability of Data Mining Techniques in Intrusion Detection Systems

Abstract: The swift development of computers technologies changed the way data and information were kept. The risk of this data being exposed to uninvited and unauthorized users arises with this new paradigm of data access. Numerous systems have been created that examine data to look for deviations from a user's or system's typical behavior or look for a known signature in the data. Intrusion Detection Systems (IDS) is the name given to these systems. These systems use a variety of approaches, including machine learning algorithms and statistical methodologies. With the massive rise in the use of network-based services and information sharing on networks, network security has emerged as the fundamental component. The integrity, confidentiality, and availability of computer and network resources are all seriously compromised by intrusion, which also poses a severe risk to network security. Network audit data classification by humans is a costly, time-consuming, and laborious task. An intrusion detection system (IDS) is one tool used to find anomalies and attacks on a network. The network intrusion detection system has made extensive use of data mining techniques to extract valuable information from vast amounts of network data. This work proposes a hybrid model that combines two distinct intrusion detection techniques: anomaly-based and signaturebased. The model is separated into two stages. Systems for detecting intrusions make use of audit data produced by network devices, operating systems, and application software. These sources generate enormous databases that contain tens of millions of records. Data mining, which is the process of extracting meaningful patterns from a sizable amount of information, is used to analyze this data. The presented paper deals with the role and the applicability of data mining techniques in designing and developing the IDS Systems.

The Application of Big Data Technology in Teaching College Students’ Mental Health Education in Colleges and Universities

Abstract Advances in big data technology herald a new era for mental health education in higher education, offering novel solutions to age-old challenges in student psychological care. This paper investigates how big data’s analytical capabilities can surpass traditional, one-size-fits-all mental health assessments by leveraging detailed student data for personalized care. We applied data mining techniques to the mental health data of 1,200 students from College G, creating a rich database of psychological patterns and a mental health information exchange platform. The analysis led to the identification of 500 instances of psychological distress with an accuracy of 79.5% and unveiled patterns linking academic stress and adjustment difficulties to mental health issues. Our research underscores big data’s role in enhancing mental health interventions, providing the groundwork for more individualized and effective mental health services in academic settings.

Prediction model of a shell and tube heat exchanger based on the technique of artificial neural networks

The purpose of this paper is to report the proposal of a temperature prediction model methodology for a shell and tube heat exchanger using Artificial Neural Networks (RNAs). For the generation of the model, a set of historical data of five years was used, where 1633 readings from the equipment were obtained. The recorded data were the heat transfer coefficients and the fuel flow to predict the fluid temperature. The proposed methodology uses three stages. The first was the scaling of the data set between 0 and 1, this was done to facilitate the training of the RNA model: The second was to apply data mining techniques to create data clusters to model the behavior of the heat exchanger. The last stage was the evaluation of the prediction models. 5 proposals for Neural Network models were evaluated, these used 10 neurons in the hidden layer, the main difference between them was the number of clusters used in the training data that increased one by one. The average training errors obtained by the four types of data pools were 0.00220, 0.00190, 0.00133, 0.00098, and 0.00080. According to the results obtained, it was possible to conclude that the model that uses a greater number of clusters has a lower prediction error.

Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes—a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model’s effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection.

Classifying Severity of Unhealthy Air Pollution Events in Malaysia: A Decision Tree Model

The application of data mining technique in dealing with real problems is popular and ubiquitous in various knowledge domains. This study proposes the concept of severity measures correspond to the characteristics of duration and intensity size for evaluating unhealthy air pollution events. In parallel with that, the present study also proposes a decision tree as a predictive model to deal with a binary classification corresponding to extreme and non-extreme unhealthy air pollution events, which is established based on threshold of the power-law behavior. In a similar vein, other characteristics, such as duration and intensity size, were also determined as important related features. A case study was conducted using the air pollution index data of Klang, Malaysia, from January 1st, 1997 to August 31st, 2020. The results found that the decision tree model can provide a high degree of precision and generalization with 100% accuracy in classifying a class for extreme and non-extreme events for the air pollution severity in the Klang area. In addition, a duration size is the most influential feature that leads to the occurrence of an extreme air pollution event. Thus, this study also suggests that authorities should exercise some vigilance precautions with respect to pollution incidents with a consecutive duration exceeding 11 hours.

Application of data mining techniques for assessment of fracture load and energy in double cantilever beam solder joints

Predicting the exact values of fracture load and energy has a significant effect in preventing solder joint failure. In this research, various double cantilever beam (DCB) solder joints with different geometric constraints, including adherend thickness, adherend width, loading arm length, and solder thickness were tested under mode I crack propagation and at a strain rate of 0.03 . According to ANOVA test results, all the mentioned geometric factors influence the fracture load, but adherend width and solder thickness don't change the failure energy remarkably, considering the type I error is equal to 0.05. The failure load and energy forecasting using the random forest algorithm showed that the prediction accuracy is 92% and 81% respectively. Linear regression and lasso regression were also utilized to identify the solder joint's fracture behavior. The coefficient of determination in both methods is acceptable for solder joint fracture force prediction, but for fracture energy, it has decreased to about 70%. Finally, multi-objective optimization was done with the help of the NSGA-II method, and the Pareto front diagram drawn according to the problem's constraints to find the optimal values for fracture force and energy.

Applying data mining techniques to predict vitamin D deficiency in diabetic patients.

Vitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.

APPLICATION OF DATA MINING IN HEALTHCARE OF BANGLADESH

The paper intends to discuss the status of data mining applications in the normal and critical healthcare system of Bangladesh. The paper also focuses on the data mining strategies and processes in the current healthcare system in Bangladesh. This is a secondary source-based review paper. The methodology chosen for the study is a literature review and document analysis. The literature review was done through the analysis of book chapters, research-based articles, review-based journal articles, organizational reports, and conference papers. The key findings of the study indicate that as Bangladesh is not a developed country and is a developing one, the country’s healthcare system is not up to the required standard, and the application of data mining techniques is very limited and in most cases, it is at the policy level. The implementation and practice of data-mining techniques is available in internationally recognized organizations and institutions. In these circumstances, the country’s healthcare policymakers and administrators should employ experts in data mining in healthcare services and facilities and standard technologies should be included in their administering the data mining in healthcare facilities. Besides, data mining should be incorporated with other related concepts and strategies to make the highest outcomes in health care systems.

Literature Study on Predicting Gold Prices using Machine Learning

Gold is a metal that is believed to be able maintain prices and can be a medium of exchange that has different forms and functions. In addition, gold can be used as a long-term investment tool at various prices. Price variations can be influenced by many factors, so predicting gold prices can minimize risk. Gold price predictions are not only interesting for collectors but also interesting for analysts to discuss. Machine learning is one method that is often used to predict gold prices. In this study will discuss the study of literature based on the methods and results of previous literature. The purpose of this study is to determine the best performance of the methods that have been used and can be used as a reference in predicting gold prices. The purpose of writing this journal is to provide an overview as well the benefits of applying data mining techniques to machine learning. These benefits include development better understanding of machine learning, as well as improved decision making and technological innovation.

Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms: Market Basket Analysis to Discover Rules of Item Combinations

This study focuses on applying data mining techniques, especially association rules mining using the Apriori and FP-GROWTH algorithms, for market basket analysis on PT. XYZ is a pharmaceutical company in Indonesia. A quantitative methodology uses a dataset of 100,498 transactions originating from 432,356 rows of data covering July to December 2022 in the JABODETABEK area. Apriori and FP-GROWTH algorithms are applied for association rules mining. The results show that FP-GROWTH has the fastest execution time of 84,655 seconds. However, the memory usage for the Apriori algorithm is the lowest at 482.32 MiB, with increments of: 0.21 MiB. For the rules generated, the two algorithms, both Apriori and FP-GROWTH, produce the same number of rules and values of support, confidence, lift, Bi-Support, Bi-Confidence, and Bi-Lift. In conclusion, Apriori is recommended for sales datasets if memory usage and ease of implementation are important. However, if the speed of execution time and a large amount of data are considered, FP-GROWTH is a better choice because the execution time is faster for large amounts of data. However, the choice of algorithm depends on the specific analysis objectives, itemset size, data scale, and computational capabilities. Results from association rules mining provide evidence of product popularity, purchasing patterns, and opportunities for strategic marketing and inventory management. These findings can help PT. XYZ improves business efficiency, understands customer behavior, and increases profitability.

Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall-snowmelt regime Munzur River, Türkiye.

Revealing the dynamic link between rainfall and runoff, which are the main components of the hydrological cycle, is significant for the planning and managing water resources, disaster risk management, and construction of water structures. This study used feed-forward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), and long short-term memory (LSTM) network to model the rainfall-runoff relationship. Various variations of lagged precipitation, temperature, relative humidity, and flows were presented as inputs, and the flow values of Munzur River were estimated as outputs. During the selection of input parameters, variables with high correlation to flow values were utilized. The model's success was tested using several statistical indicators, including the coefficient of correlation (R), coefficient of determination (R2), and root mean square error (RMSE). When measuring values and model results are compared, FFNN and ANFIS models show accurate predictive results with high accuracy, while LSTM prediction results are not satisfactory. However, it was concluded that the FFNN model with the hyperbolic tangent sigmoid transfer function and Levenberg-Marquardt training algorithm made a slightly more accurate estimation. In addition, it was revealed that the best ANFIS-Sugeno model was obtained with a hybrid learning algorithm, Gaussmf membership function, and eight subsets. As a result of the analysis, it has been found that FFNN is superior to ANFIS in flow prediction. These results provide policymakers and planners with helpful information for developing flood and drought management strategies.

Optimal Variable Selections and Prediction Methods for Soil Chemical Property Using Data Mining and Machine Learning Approaches

The main objective of the research is to convert raw data into actionable knowledge. Creating a decision tree involves recursively dividing the data into subsets based on different attributes, aiming to achieve homogeneity or minimize variance within each subset. This paper considers agriculture and its soil chemical-related dataset for applying data mining techniques to find suitable variables for future predictions. The tenfamiliar ML approaches are Gaussian processes,linear regression, multilayer perceptron, simple linear regression, SMOreg, decision stump, M5P, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

Applying data mining techniques to predict the market performance of publicly traded companies

Applying data mining techniques to predict the market performance of publicly traded companies

Web-Based Motorcycle Loan Management System with credit risk and approval analysis using Data Mining Techniques

Using information systems can dramatically improve a business's productivity and efficiency. Moreover, the ability of information systems to provide users with the information they need to complete jobs effectively can be the most significant advantage. This project development plan proposes a Web-Based Motorcycle Loan Management System with credit risk and approval analysis using Data Mining Techniques for a private motorcycle dealer who encounters challenges, particularly in the decision-making of motorcycle loans. It will facilitate the automation of business processes, including online motorcycle loan applications and payments, access to real-time data and reporting, and credit risk and approval analysis to assist the credit department in decision- making by applying data mining techniques. The project will employ the Scrum framework for software development and testing method. A total of 150.625 man-days with a total cost of P1,350,231.78 were computed to complete the proposed information system. A one-year financial viability analysis was defined to determine the proposed project's total cost and benefits. Based on the results, time spent on activities is reduced to 89.19%, indicating that the business can handle more applications every day compared to the average twenty daily applications since processing time is significantly decreased. It is recommended for future improvement of this plan to implement Augmented Reality for a better user online experience in previewing motorcycle units.

APLIKASI MARKET BASE ANALITIK UNTUK MEMPELAJARI POLA BELANJA MASYARAKAT PADA TOKO UD GARAM KUSTONO MENGGUNAKAN ALGORITMA APRIORI

At this time the transaction data at the UD Garam Kustono store has decreased, this shows that thereis an increase in sales competition followed by an increase in applying information technology. Toovercome these problems, a very appropriate sales strategy is needed to use the availability of usefuldata and information. In order for data from sales transactions to be more efficient, it is necessary toprocess data by applying data mining techniques. The method used by researchers is the a priorialgorithm. The aim of this study is to apply a data mining system to find information oninterrelationships between products that can support sales strategies from transaction data. Thisinformation will be used to support sales strategy decisions by adjusting the products to be offeredto buyers. The results of this study will show that this a priori algorithm is used to process salestransaction data and become information in the form of links between products that are most oftenpurchased by the public.

Analyzing the Impact of Epidermoid and Adeno Tissue on Cancer Incidence Whit a Data Mining Approach

Early detection is the only way to effectively control diseases whose treatment can be challenging, expensive, and time-consuming. Identifying the influencing factors in the occurrence of disease can, therefore, reduce the time associated with diagnosis and provide a solid foundation for improving the prognosis and preventing patients' deterioration. Applying data mining techniques as a novel approach to the early detection of disease-causing agents can significantly assist the early detection. In this study, an attempt was made to investigate the effect of epidermoid and adeno tissues on the incidence of cancerous diseases such as bone, bone marrow, lung, and neck cancer by conducting a data mining process on cancer patient data sets. Hence, Implementing two data mining techniques, K-nearest neighbor and decision tree, on the data of patients with these four types of cancer, an attempt was made to evaluate their performance using the three criteria of accuracy, error ratio, and negative prediction value. The implementation of data mining techniques and evaluations of their performance indicates that the decision tree technique performed better with an accuracy of 89.10%, an error ratio of 14.04%, and negative prediction value of 77.71%. Also, based on the findings, contamination of epidermoid and adeno tissues does not affect the early detection of any of the four categories of bone, bone marrow, lung, or neck cancer. In other words, the infection of the two epidermoid and adeno tissues cannot be the cause of the four types of bone, bone marrow, lung, and neck cancer

Prediction of the mechanical behavior of mortars incorporating phase change materials using data mining techniques

Nowadays, it is imperative to reduce the energy bill in order to contribute to a more sustainable planet. In this sense, the use of materials that contribute to the energy efficiency of buildings is a very important contribution to achieve this goal. Mortars incorporating phase change materials (PCM) can make an important contribution to this end, due to its thermal storage capacity, increasing the energy efficiency of buildings. In this work several mortars with different PCM contents were developed, using different binders (cement, aerial lime, hydraulic lime and gypsum). The aim of this study was to apply data mining techniques such as artificial neural networks (ANN), support vector machines (SVM) and multiple linear regressions (MLR) to forecast the compressive and flexural strengths of these mortars at different exposure temperatures. It was concluded that ANN models have the best predictive capacity both for compressive strength and flexural strength. However, the SVM models have a flexural strength forecasting capacity very close to ANN models.