Mining Approach Research Articles

Genome mining approach reveals the CRISPR-Cas characteristics and diversity of targeting phages in Lactobacillus iners strains

This study utilized a genomic approach to investigate CRISPR-Cas systems, focusing on Lactobacillus iners strains, which are crucial for bacterial defense against bacteriophage attacks. Genome sequences were analyzed to understand the diversity, occurrence, and evolution of these systems. Spacer sequences within CRISPR arrays were examined to identify targeted phages. The research explored the evolutionary paths of spaceromes within each CRISPR array subtype, considering acquisition and deletion events influenced by phage pressure. Results revealed that approximately 50 % of L. iners strains possess complete CRISPR-Cas systems, predominantly subtype II-A and I-E. Homology analysis indicated that subtype I-E systems target a wider range of foreign phages compared to subtype II-A systems. Overall, the findings underscore the varied components and structure of CRISPR-Cas systems in L. iners strains, highlighting their role as active immune defenses against phages and foreign DNA.

An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.

Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various techniques, including random forest, ExtraTrees, XGBoost, artificial neural network (ANN), and genetic algorithm with ANN (GANN) were applied on two benchmark datasets to predict stroke based on several parameters, such as gender, age, various diseases, smoking status, BMI, HighCol, physical activity, hypertension, heart disease, lifestyle, and others. Due to dataset imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied to the datasets. Hyperparameter tuning optimized the models via grid search and randomized search cross-validation. The evaluation metrics included accuracy, precision, recall, F1-score, and area under the curve (AUC). The experimental results show that the ensemble ExtraTrees classifier achieved the highest accuracy (98.24%) and AUC (98.24%). Random forest also performed well, achieving 98.03% in both accuracy and AUC. Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior performance, indicating its potential as a promising method for stroke prediction and offering substantial benefits to healthcare.

Combination of Lexical Resources and Support Vector Machine for Film Sentiment Analysis

Text data generated by internet users holds potentially valuable information that can be researched for new insights. One strategy for obtaining information from a text data set is to classify text into predetermined categories based on existing data. Text classification is an aspect of Text Mining. One of the popular approaches in Text Mining uses the Support Vector Machine (SVM) classification algorithm, which aims to classify text and separate data into different classes. However, in some cases, SVM classification algorithms may face difficulties in understanding the context of the text properly due to unclear wording, varying sentence structures, or a lack of understanding of interpretation. To address this problem, applying SVM classification using lexical resources can be an effective solution. In this research framework, the first step is to obtain data, which in this case is a film review dataset taken from the kaggle.com site. After obtaining the data, the next step is preprocessing. The results of the preprocessing are then divided into 80:20 percentages. The 80% training data is used to search for the form of polarization, and this training data lexicon is used for training the SVM model. Based on the modeling results, the overall model accuracy is around 85%, calculated using the confusion matrix. The precision value, which shows the proportion of correct positive predictions, reached 88%. The precision for negative predictions reached 80%, and for neutral predictions, it reached 0%. These results show that the Lexicon+SVM model has good performance, with an accuracy of 85%.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

A Predictive Model for Academic Performance of Students in MAT101 at Federal University Birnin Kebbi

The research addresses the problem of low academic performance of students taking General Mathematics 1 (MAT101) at the Federal University Birnin Kebbi in Nigeria. MAT101 is a foundational course for several science-related disciplines. However, many students fail to understand its concepts, translating to one of the lowest academic performances, leading to high discontinuation of students’ studies in most science and related fields. To this end, the problem is addressed through a predictive model using data science and machine learning techniques to identify students who are academically at risk. It seeks to answer the appropriateness of the selected statistical and data mining approaches, whether or not they are predictor variables, and validating the model developed. The significance of this study lies in its potential to help instructors identify at-risk students and improve overall performance in MAT101. The research uses data collected from academic records and conducts exploratory data analysis to determine the factors influencing students’ performance. Several classifiers such as Decision Tree and Random Forest were analyzed by evaluating their accuracy. In detail, the difference in the accuracy for the Decision Tree algorithm was 52% and for the Random Forest was 67%. Based on achieved indicators, the Random forest algorithm is more effective in this type of predictive modeling. However, the choice of algorithm depends on specific task requirements, considering factors like speed and accuracy.

A Multi-dimensional Data Mining-based Study on the Prescriptions Developed by Professor Xu Zhiyin in Treating Thyroid Nodules.

In this study, we employed a multi-dimensional data mining approach to examine the clinical instances where Professor Xu Zhiyin treated thyroid nodules. Our aim is to understand the patterns of symptoms, underlying causes, and treatment approaches used for thyroid nodules. By doing so, the intention is to distill the essential aspects, compile Professor Xu Zhiyin's clinical insights, and investigate his scholarly perspectives. Professor Xu Zhiyin's clinical diagnoses and treatments spanning from 2009 to 2019 were entered into Microsoft Excel. Subsequently, the collected data was imported into the Medcase V5.2 system to facilitate data mining. Various techniques, such as frequency-based method, association rule analysis, and clustering, including a decentralized system clustering approach, were employed on a set of 346 cases involving patients with thyroid nodules that conformed to the specified criteria. The primary focus was on extracting insights regarding symptoms and the underlying causes from the medical records. By integrating these findings with Professor Xu Zhiyin's clinical expertise, we examined and summarized the outcomes of the data mining process. The fundamental prescriptions were successfully extracted using the techniques for mining across multiple dimensions. Utilizing the scattered grouping of these prescriptions and with reference to the cluster analysis of the frequency-linked system, the fundamental prescriptions proposed by Professor Xu Zhiyin for addressing thyroid nodules encompass the following ingredients: Glycyrrhiza uralensis Fisch, Cortex Moutan, Paeoniae radix rubra, Curcuma longa L., Radix Curcumae, persica seed, Citri Reticulatae Viride Pericarpium, Pinellia ternata, Spica Prunellae, Ostreae concha, Gleditsia sinensis spine, Tuckahoe and Radix Codonopsis. The fundamental prescriptions were acquired using the frequency approach, association rule technique, k-means clustering approach, and systematic clustering approach. The research findings corroborate one another, demonstrating that Professor Xu Zhiyin's approach to distinguishing and treating thyroid nodules is embodied in distinct prescriptions tailored to specific diseases.

A Secure Parallel Pattern Mining System for Medical Internet of Things.

In this paper, a new generic parallel pattern mining framework called multi-objective Decomposition for Parallel Pattern-Mining (MD-PPM) is developed to solve challenges in the Internet of Medical Things through big data exploration. MD-PPM discovers important patterns by using decomposition and parallel mining methods to explore connectivity between medical data. First, a new technique, the multi-objective k-means algorithm, is used to aggregate medical data. A parallel pattern mining approach based on GPU and MapReduce architectures is also used to create useful patterns. To ensure complete privacy and security of the medical data, blockchain technology has been integrated throughout the system. Several tests were conducted to demonstrate the high performance of two sequential and graph pattern mining problems on large medical data and to evaluate the developed MD-PPM framework. From our results, our proposed MD-PPM has achieved strong results in terms of memory usage and computation time in terms of efficiency. Moreover, MD-PPM performs well in terms of accuracy and feasibility compared to existing models.

Mapping a Nanomedical Thematic Network in Breast Cancer: Text Mining Approach

Objectives: Today, the use of nanotechnology in medical practice is increasingly growing. One of the practical aspects of nanomedicine is its use in breast cancer. Using text mining techniques, analysis of research in this field will help draw its thematic network and discover the main thematic areas under study. Methods: This research was conducted using the text mining method in January 2022. The present study population consisted of scientific articles on breast cancer by researchers in the field of nanomedicine that have been published in scientific journals around the world, and their bibliographic information has been indexed in the Medline database. A total of 838 records were recovered in this research. Data analysis was done using VOSviewer software, version 1.6.17. Results: Based on the thematic network obtained from the analysis of the titles and abstracts of the studied documents, concepts such as paclitaxel, patient, Michigan Cancer Foundation (MCF), poly, imaging and toxicity are among the most widely used research topics in the field of nanomedicine in breast cancer internationally. The study of the thematic network at different periods shows changes in the concepts related to this field. "MCF," "poly," "imaging" and "toxicity" concepts were the most active subject areas in 2019-2021, but "paclitaxel" and "patients" concepts were the most active in 2015-2018. Discussion: The textual analysis clearly shows that nanomedicine in breast cancer has become more of a treatment than a diagnosis, and in the near future, researchers will focus on the fundamental goals of breast cancer treatment and ways to deal with it.

How do tourists perceive churches as tourism destinations? A text mining approach through online reviews

AbstractChurch tourism has gained popularity due to the historical, artistic, and religious significance of churches, contributing significantly to the social and economic sectors. The destination image of churches plays a crucial role in tourism marketing. However, existing literature predominantly relies on traditional methods to study tourists' perception of churches as tourism destinations, with limited analysis of user‐generated content (UGC) like online reviews. This study aims to fill this gap by analyzing the online reviews of 10 renowned European churches posted on TripAdvisor using text mining techniques. Findings reveal that tourists mainly focus on overall description, visiting experience, architectural design, and surrounding environment of the church, with specific areas of interest for certain churches. The research findings can assist church administrators in formulating sustainable tourism strategies and promoting the development of the church tourism.

Determining consumer demand patterns for production planning using a data mining approach

Determining consumer demand patterns for production planning using a data mining approach

Modeling performance evaluation in badminton sports: a fuzzy logic approach

Spectators and many young students have flocked to badminton matches in recent years. Badminton practice has received a lot of media coverage. The current state of badminton evaluation methods is lacking in reliability. This article's overarching goal is to examine the many applications of fuzzy logic in badminton performance evaluation and improvement. Data on the badminton technique's flexion and extension phases are mapped into the suggested model using a fuzzy inference system (FIS). This study suggests a fuzzy logic-based badminton-specific objective fuzzy inference system (Bmt-FIS) to evaluate team sports. Despite the gravity of the situation, decisions involving performance reviews often use subjective data. These common decision-making problems may be realistically addressed by fuzzy logic models. Fuzzy logic has the potential to be an effective tool in situations where both quantitative and qualitative data interpretation are allowed. To do this, it accounts for the inherent variability in athletic performance by taking into consideration the 'hazy' or 'uncertain' limitations of data. By taking limitations into account, a rule-based approach makes performance evaluation more precise. Here, a fuzzy inference system (FIS) uses the input variables to evaluate the student's performance. While data mining approaches have been studied, the adaptive neural fuzzy method outperforms others because of its exceptional accuracy.This method eloquently and clearly conveys the many levels of integrity and ambiguity. Also, fuzzy logic may be a great tool for evaluating badminton skills. This foundational study connects the dynamic realm of sports with static measures.

Prediction Model of School Drop Out Factors Using Classification Techniques in Selangor

Malaysia has been struggling to sustain the number of students graduating from school, with an increasing number leaving early, which poses significant concerns for the future generation. Several factors contribute to this issue, such as economic constraints, geographical challenges, transportation problems, and sociocultural norms. This paper uses a data mining approach to identify the attributes that lead to school dropouts and to determine the predictive model with the highest accuracy for forecasting dropout rates. The application of data mining approaches has proven effective in predicting students at risk of dropping out in general education. Nevertheless, there is a shortage of data mining-related studies on student attrition in public schools in Malaysia. The study utilized student and school datasets with consent from the respective departments in the Ministry of Education. This data includes information from 2,482 students across various primary schools in Selangor with initially 22 attributes collected from the dataset. After the attributes undergone feature selection process by using InfoGainAttributeEval, there are 12 features left including class attribute Status_DO. The collected data encompasses student demographics, academic performance and socioeconomic background. The experiments for this study used Decision Trees (J48), Naïve Bayes and Random Forest. By using classification techniques that were made available in WEKA, all attributes from the dataset were tested. The results of the analysis shown that Random Forest with the highest accuracy of 79.5729% in term of predicting student drop out hence indicate the reliability of this research as a decision support tool.

Service quality dimensions in Austrian food retailing – a text mining approach for physical retail stores

ABSTRACT Investigating Service Quality (SQ) and its implications for customer satisfaction has become an increasingly popular research area. Especially in the context of physical grocery retailing, ensuring customer satisfaction has become a key success factor. Most previous research studies apply traditional methods (e.g. surveys) for physical shops and utilize text mining-based approaches mainly for e-commerce. In our paper, we propose a novel approach based on LDA. By combining expert-based word counting analysis with the LDA approach, we confirm that unsupervised text mining based on LDA can be used as a reliable approach to cluster textual comments to SQ dimensions in physical retail settings. We have analyzed over 163,000 publicly available textual customer reviews related to the Austrian market, which is special in terms of its extremely high density of retail outlets, high price levels and a tendency towards traditional form of shopping. Our results show that personal interaction, policies, and product-related aspects seem to be positive drivers of customer satisfaction, while reliability is a clear driver of customer dissatisfaction. Results also show a significantly higher relevance of personal interaction in smaller stores and cities with fewer than 5,000 inhabitants than in other store types and bigger cities. Regarding practical implications, hypermarkets should focus on physical aspects to reduce negative reviews and increase efforts in personal interaction to increase positive reviews. On the other hand, smaller stores should continue to rely on personal interaction to avoid negative reviews and might focus on higher reliability to generate more positive reviews. The applied text-mining approach enables future research with a starting base to analyze SQ dimensions and their relevance in additional countries or area, as e.g. fashion or hardware retail.

Enhancing healthcare process analysis through object-centric process mining: Transforming OMOP common data models into object-centric event logs

Objectives:This study aims to enhance the analysis of healthcare processes by introducing Object-Centric Process Mining (OCPM). By offering a holistic perspective that accounts for the interactions among various objects, OCPM transcends the constraints of conventional patient-centric process mining approaches, ensuring a more detailed and inclusive understanding of healthcare dynamics. Methods:We develop a novel method to transform the Observational Medical Outcomes Partnership Common Data Models (OMOP CDM) into Object-Centric Event Logs (OCELs). First, an OMOP CDM4PM is created from the standard OMOP CDM, focusing on data relevant to generating OCEL and addressing healthcare data’s heterogeneity and standardization challenges. Second, this subset is transformed into OCEL based on specified healthcare criteria, including identifying various object types, clinical activities, and their relationships. The methodology is tested on the MIMIC-IV database to evaluate its effectiveness and utility. Results:Our proposed method effectively produces OCELs when applied to the MIMIC-IV dataset, allowing for the implementation of OCPM in the healthcare industry. We rigorously evaluate the comprehensiveness and level of abstraction to validate our approach’s effectiveness. Additionally, we create diverse object-centric process models intricately designed to navigate the complexities inherent in healthcare processes. Conclusion:Our approach introduces a novel perspective by integrating multiple viewpoints simultaneously. To the best of our knowledge, this is the inaugural application of OCPM within the healthcare sector, marking a significant advancement in the field.

A text mining approach to explore factors influencing consumer intention to use metaverse platform services: Insights from online customer reviews

Metaverse technology has recreated real-world surroundings into a virtual setting that has caught the attention of many consumers and brands. Metaverse platforms are facilitating brands to sell physical and digital versions of their products, and consumers are eagerly purchasing these products. Owing to this dramatic shift, it is crucial to understand the factors impacting user adoption of metaverse platform services. The present study leverages an LDA-based topic modeling approach to analyze 17136 online user reviews of the Highrise Virtual Metaverse mobile platform. The study revealed ten factors impacting consumer intention to use the metaverse platform services that are asset buy-sell experience, availability of virtual assets, visually appealing virtual environment, enjoyment from virtual events, social interaction, social activity, positive experience with the metaverse ecosystem, mobile app-related issues, user account-related issues, and the feature of watch ad-to earn. Building upon the uses and gratification theory and flow theory, the study explained that consumers gain three major gratifications and experience a flow state while using metaverse platform services. Furthermore, it explores the emotions expressed by users within the metaverse platform. Correspondence analysis is performed to understand the factors leading to positive and negative user experiences with the platform services. The study presents crucial practical implications for brand marketers and metaverse platform developers.

Sustainable Green Educational Paths in the Italian Higher Education Institutions: A Text Mining Approach

In response to the global imperative of environmental sustainability, higher education institutions play a pivotal role in shaping a green-conscious society. This study delves into the landscape of Italian university education, analyzing the green educational offer and examining the degree programs with a dedicated focus on environmental sustainability promoted during the academic year 2021–2022. For this purpose, we employed statistical-textual analyses techniques to examine the texts describing the educational objectives and the expected professional outcomes after the completion of the study paths for 179 Italian green degree courses (BA and MS), analyzing a corpus resulting in 144,709 occurrences. In particular, the multifaceted nature of the green educational approach, including its multidisciplinary and interdisciplinary orientation, attention to territorial dynamics, intersectoral collaboration, and the balance between theoretical and applied training, are studied. The results reveal an integration of these crucial elements within the Italian university educational offer. This research addresses the growing importance of environmental sustainability in higher education and provides insights into how Italian universities contribute to this global discourse. The findings contribute to ongoing discussions on refining educational strategies, aligning with global sustainability goals, and fostering a generation of environmentally conscious and skilled professionals.

Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application

Most process mining techniques are primarily automated, meaning that process analysts input information and receive output. As a result, process mining techniques function like black boxes with limited interaction options for analysts, such as simple sliders for filtering infrequent behavior. Recent research tries to break these black boxes by allowing process analysts to provide domain knowledge and guidance to process mining techniques, i.e., hybrid intelligence. Especially, in process discovery—a critical type of process mining—interactive approaches emerged. However, little research has investigated the practical application of such interactive approaches. This paper presents a case study focusing on using incremental and interactive process discovery techniques in the healthcare domain. Though healthcare presents unique challenges, such as high process execution variability and poor data quality, our case study demonstrates that an interactive process mining approach can effectively address these challenges.Graphical

Patterns and factors associated with dental service utilization among insured people: a data mining approach

BackgroundInsurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective of this study was to identify patterns and factors influencing the utilization of dental services among complementary insured individuals, employing a data mining methodology.MethodsA secondary data analysis was conducted using a dental insurance dataset from Iran in 2022. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was employed as a data mining approach for knowledge extraction from the database. The utilization of dental services was the outcome of interest, and independent variables were chosen based on the available information in the insurance dataset. Dental services were categorized into nine groups: diagnostic, preventive, periodontal, restorative, endodontic, prosthetic, implant, extraction/surgical, and orthodontic procedures. The independent variables included age, gender, family size, insurance history, franchise, insurance limit, and policyholder. A multinomial logistic regression model was utilized to investigate the factors associated with dental care utilization. All analyses were conducted using RapidMiner Version 2020.ResultsThe analysis encompassed a total of 654,418 records, corresponding to 118,268 insured individuals. Predominantly, restorative treatments were the most utilized services, accounting for approximately 38% of all services, followed by diagnostic (18.35%) and endodontic (13.3%) care. Individuals aged between 36 and 60 years had the highest rate of utilization for any dental services. Additionally, families comprising three to four members, individuals with a one-year insurance history, people contracted with a 20% franchise, individuals with a high insurance limit, and insured individuals with a small policyholder, exhibited the highest rate of service usage compared to their counterparts. The regression model revealed that all independent variables were significantly associated with the use of dental services. However, the patterns of association varied among different service categories.ConclusionsRestorative treatments emerged as the most frequently used dental services among insured individuals, followed by diagnostic and endodontic procedures. The pattern of service utilization was influenced by the characteristics of the insured individuals and attributes related to their insurance.

NMR resonance assignment of the cell death execution domain BELL2 from multicellular bacterial signalosomes.

Signalosomes are high-order protein machineries involved in complex mechanisms controlling regulated immune defense and cell death execution. The immune response is initiated by the recognition of exogeneous or endogenous signals, triggering the signalosome assembly process. The final step of signalosome fate often involves membrane-targeting and activation of pore-forming execution domains, leading to membrane disruption and ultimately cell death. Such cell death-inducing domains have been thoroughly characterized in plants, mammals and fungi, notably for the fungal cell death execution protein domain HeLo. However, little is known on the mechanisms of signalosome-based immune response in bacteria, and the conformation of cell death executors in bacterial signalosomes is still poorly characterized. We recently uncovered the existence of NLR signalosomes in various multicellular bacteria and used genome mining approaches to identify putative cell death executors in Streptomyces olivochromogenes. These proteins contain a C-terminal amyloid domain involved in signal transmission and a N-terminal domain, termed BELL for Bacteria analogous to fungal HeLL (HeLo-like), presumably responsible for membrane-targeting, pore-forming and cell death execution. In the present study, we report the high yield expression of S. olivochromogenes BELL2 and its characterization by solution NMR spectroscopy. BELL is folded in solution and we report backbone and sidechain assignments. We identified five α-helical secondary structure elements and a folded core much smaller than its fungal homolog HeLo. This study constitutes the first step toward the NMR investigation of the full-length protein assembly and its membrane targeting.

The Assessment of the Association of Proton Pump Inhibitor Usage with Chronic Kidney Disease Progression through a Process Mining Approach.

Previous studies have suggested an association between Proton Pump Inhibitors (PPIs) and the progression of chronic kidney disease (CKD). This study aims to assess the association between PPI use and CKD progression by analysing estimated glomerular filtration rate (eGFR) trajectories using a process mining approach. We conducted a retrospective cohort study from 1 January 2006 to 31 December 2011, utilising data from the Stockholm Creatinine Measurements (SCREAM). New users of PPIs and H2 blockers (H2Bs) with CKD (eGFR < 60) were identified using a new-user and active-comparator design. Process mining discovery is a technique that discovers patterns and sequences in events over time, making it suitable for studying longitudinal eGFR trajectories. We used this technique to construct eGFR trajectory models for both PPI and H2B users. Our analysis indicated that PPI users exhibited more complex and rapidly declining eGFR trajectories compared to H2B users, with a 75% increased risk (adjusted hazard ratio [HR] 1.75, 95% confidence interval [CI] 1.49 to 2.06) of transitioning from moderate eGFR stage (G3) to more severe stages (G4 or G5). These findings suggest that PPI use is associated with an increased risk of CKD progression, demonstrating the utility of process mining for longitudinal analysis in epidemiology, leading to an improved understanding of disease progression.