Improvement of Association Algorithm and Its Application in Audit Data Mining

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and their Association with Mortality through a Frequent Pattern Growth Association Algorithm. (Preprint)

BACKGROUND Chronic diseases are responsible for most health problems in older people. We know that chronic conditions tend to cluster in the form of patterns, also known as multimorbidity patterns. However, health systems and professionals are generally organized and trained to respond to specific diseases independently, negatively impacting patients and health systems. Different initiatives are trying to respond to these problems. In this context, the current availability of electronic health records and other types of health research data represents an excellent research opportunity. However, there are also some relevant limitations and challenges related to a current lack of tools that allow us to access, harmonize, integrate and reuse datasets technically, legally, ethically, and respectfully to patients and society. In this sense, the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles can help us to guide scientific data management and stewardship and drive scientific discovery to a new paradigm. FAIR4Health is a European Commission supported project that applies FAIR principles on publicly-funded research datasets. OBJECTIVE To present the FAIR4Health pathfinder case study designed to validate and evaluate the FAIR4Health solution with the aim of identifying multimorbidity patterns and their association with mortality in older adults from different health organizations databases of four European countries. METHODS To apply the FAIR principles in five European cohorts from different healthcare settings (i.e., primary care, hospitals, and nursing homes) and institutions (i.e., University of Geneva from Switzerland, Università Cattolica del Sacro Cuore from Italy, University of Porto from Portugal, Instituto Aragonés de Ciencias de la Salud from Spain, and Andalusian Health Service also from Spain), a multicentric retrospective observational study (N = 11,034) was performed. In FAIR4Health, a workflow was designed to implement the FAIR principles on health datasets, and two tools were developed, a Data Curation Tool to transform the raw datasets into FAIR datasets and a Data Privacy Tool to preserve data privacy. On top of these, the FAIR4Health Platform was implemented to provide an interface for researchers, and enable the usage of federated machine learning algorithms on FAIR datasets. In this study, we applied a federated frequent pattern growth association algorithm to identify the most frequent disease patterns among a set of variables. RESULTS We applied the FAIR principles in the health research datasets from different organizations, and we were able to reuse and integrate heterogeneous datasets, increasing the variability of data compared to the studies not applying those principles. We identified and described high-frequent multimorbidity patterns consistent with the literature and observed a strong association with polypharmacy and mortality. CONCLUSIONS Our results highlight the importance of implementing the FAIR data policy to overcome the difficulties in data management and accelerate responsible health research with patients and society.

Research on Risk Assessment of Multitarget Wireless Sensing Detection Auxiliary Engineering in Mine Geological Environment

In this paper, we evaluate the risk of auxiliary engineering of mine geological environment utilizing multiobjective wireless sensing detection. After vectorization of each evaluation factor, we evaluate the geological environment of a high-top mountain mine by fuzzy comprehensive evaluation method and get the evaluation results of our geological environment. Firstly, an in-depth analysis is carried out for the tracking gate that most association methods pay little attention to. In terms of optimization of traditional tracking gate, an attempt is made to propose a genetic algorithm fusion coding for tracking gate optimization, and then, the principal characteristics of traditional data association algorithm are focused on, and a particle swarm annealing association algorithm is proposed. Finally, the image information fusion of multisensors is studied, and the image information fusion level, fusion technology, and fusion process are discussed in detail, and the optimized discrete wavelet and color space alignment method is proposed, and good results are achieved through experiments. Among them, the area of poor geological environment evaluation is 0.32 km2, accounting for 6.6% of the whole study area, the area of average geological environment evaluation is 0.63 km2, accounting for 12.7% of the whole study area, and the area of good geological environment evaluation is 3.98 km2, accounting for 80.7% of the whole study area. The evaluation results can fully reflect the degree of mining influence on the Gaodingshan mining area and provide a theoretical basis for the study of mine environment restoration and management.