Towards a Domain – Specific Comparative Analysis of Data Mining Tools

Advancement in technology has brought in widespread adoption and utilization of data mining tools. Successful implementation of data mining requires a careful assessment of the various data mining tools. Although several works have compared data mining tools based on usability, opensource, integrated data mining tools for statistical analysis, big/small scale, and data visualization, none of them has suggested the tools for various industry-sectors. This paper attempts to provide a comparative study of various data mining tools based on popularity and usage among various industry-sectors such as business, education, and healthcare. The factors used in the comparison are performance and scalability, data access, data preparation, data exploration and visualization, advanced modeling capabilities, programming language, operating system, interfaces, ease of use, and price/license. The following popular data mining tools are assessed: SAS Enterprise Miner, KNIME, and R for business, Moodle Learning Analytics, Blackboard Analytics, and Canvas for education, and RapidMiner, IBM Watson Health, and Tableau for healthcare. It also discusses the critical issues and challenges associated with the adoption of data mining tools. Furthermore, it suggests possible solutions to help various industries choose the best data mining tool that covers their respective data mining requirements

Performance Analysis of Different Classification Methods in Data Mining for Diabetes Dataset Using WEKA Tool

Data mining is the process of analyzing data based on different perspectives and summarizing it into useful information. Classification is one of the generally used techniques in medical data mining. The goal here is to discover new patterns to provide meaningful and useful information for the users. Recently data mining techniques are applied to healthcare datasets to explore suitable methods and techniques and to extract useful patterns. This paper includes implementation of different classification methods, measures, analysis and comparison pertaining to diabetes dataset. A detailed performance analysis and comparative study of these methods are done, which can be further used to choose the appropriate algorithm for future analysis for the given dataset. DOI: 10.17762/ijritcc2321-8169.15036

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

Usability Log Analysis for Healthcare Devices with Software Process Mining

With the new trends in advanced healthcare equipment and innovation, the healthcare industry is now focused more on efficiency and improving quality. Devices record events in event log files that represent the program or application's actual usage. The log file of the event is like an operation history which shows what occurred in the program. Since devices have the log of real-time events, real-time processes can be tracked, and data analysed from different aspects. The details about the event log file can be used to create a process model and analyse the data to know its strengths and weaknesses. This thesis aimed to develop a tool for usability analysis used for GE healthcare. The design science approach has been used as an overall research method. To achieve the research goal, ideas was taken from already developed process mining algorithms and used in making algorithms that solves the problem of software process mining. In this thesis, already used process mining algorithm techniques were examined that can be used to answer the problem of software process mining. Software process mining was used to analyse the deployed software behavior. The study focused on making the process discovery algorithm along with structured algorithm. The outcome of the thesis was the tool that was used by the GE Healthcare to do the usability analysis on log files. The tool produces the events in the form of flow chart diagram

Supporting Governance in Healthcare Through Process Mining: A Case Study

Healthcare organizations are under increasing pressure to improve productivity, gain competitive advantage and reduce costs. In many cases, despite management already gained some kind of qualitative intuition about inefciencies and possible bottlenecks related to the enactment of patients' careows, it does not have the right tools to extract knowledge from available data and make decisions based on a quantitative analysis. To tackle this issue, starting from a real case study conducted in San Carlo di Nancy hospital in Rome (Italy), this article presents the results of a process mining project in the healthcare domain. Process mining techniques are here used to infer meaningful knowledge about the patient careflows from raw event logs consisting of clinical data stored by the hospital information systems. These event logs are analyzed using the ProM framework from three different perspectives: the control flow perspective, the organizational perspective and the performance perspective. The results on the proposed case study show that process mining provided useful insights for the governance of the hospital. In particular, we were able to provide answers to the management of the hospital concerning the value of last investments, and the temporal distribution of abandonments from emergency room and exams without reservation

Actionable insights through association mining of exchange rates: a case study

Association mining is the methodology within data mining that researches associations among the elements of a given set, based on how they appear together in multiple subsets of that set. Extensive literature exists on the development of efficient algorithms for association mining computations, and the fundamental motivation for this literature is that association mining reveals actionable insights and enables better policies. This motivation is proven valid for domains such as retailing, healthcare and software engineering, where elements of the analyzed set are physical or virtual items that appear in transactions. However, the literature does not prove this motivation for databases where items are “derived items”, rather than actual items. This study investigates the association patterns in changes of exchange rates of US Dollar, Euro and Gold in the Turkish economy, by representing the percentage changes as “derived items” that appear in “derived market baskets”, the day on which the observations are made. The study is one of the few in literature that applies such a mapping and applies association mining in exchange rate analysis, and the first one that considers the Turkish case. Actionable insights, along with their policy implications, demonstrate the usability of the developed analysis approach

Data mining approach to internal fraud in a project-based organization

Data mining is an efficient methodology for uncovering and extracting information from large databases, which is widely used in different areas, e.g., customer relation management, financial fraud detection, healthcare management, and manufacturing. Data mining has been successfully used in various fraud detection and prevention areas, such as credit card fraud, taxation fraud, and fund transfer fraud. However, there are insufficient researches about the usage of data mining for fraud related to internal control. In order to increase awareness of data mining usefulness in internal control, we developed a case study in a project-based organization. We analyze the dataset about working-hour claims for projects, using two data mining techniques: chi-square automatic interaction detection (CHAID) decision tree and link analysis, in order to describe characteristics of fraudulent working-hour claims and to develop a model for automatic detection of potentially fraudulent ones. Results indicate that the following characteristics of the suspected working-hours claim were the most significant: sector of the customer, origin and level of expertise of the consultant, and cost of the consulting services. Our research contributes to the area of internal control supported by data mining, with the goal to prevent fraudulent working-hour claims in project-based organizations

Data mining approach to internal fraud in a project-based organization

Data mining is an efficient methodology for uncovering and extracting information from large databases, which is widely used in different areas, e.g., customer relation management, financial fraud detection, healthcare management, and manufacturing. Data mining has been successfully used in various fraud detection and prevention areas, such as credit card fraud, taxation fraud, and fund transfer fraud. However, there are insufficient researches about the usage of data mining for fraud related to internal control. In order to increase awareness of data mining usefulness in internal control, we developed a case study in a project-based organization. We analyze the dataset about working-hour claims for projects, using two data mining techniques: chi-square automatic interaction detection (CHAID) decision tree and link analysis, in order to describe characteristics of fraudulent working-hour claims and to develop a model for automatic detection of potentially fraudulent ones. Results indicate that the following characteristics of the suspected working-hours claim were the most significant: sector of the customer, origin and level of expertise of the consultant, and cost of the consulting services. Our research contributes to the area of internal control supported by data mining, with the goal to prevent fraudulent working-hour claims in project-based organizations

Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely