Data Mining Applications in Engineering and Medicine

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

A Critical Survey of Data Grid Replication Strategies Based on Data Mining Techniques

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.

Predictive analytics has emerged as a powerful tool in the field of financial engineering, enabling organizations to forecast trends, optimize strategies, and mitigate risks. Data mining techniques play a crucial role in extracting meaningful patterns from large volumes of financial datasets, contributing to the development of predictive modeling techniques. The purpose of this paper is to explore the application of various data mining techniques in predictive analytics within the financial sector, with a focus on their effectiveness in risk management, fraud detection, and portfolio optimization. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are increasingly being employed to model complex financial systems and enhance predictive accuracy. Additionally, techniques like clustering, association rule mining, and time series analysis are used to uncover hidden relationships and temporal patterns in financial datasets. Despite the significant advancements in these techniques, challenges remain in terms of data quality, computational complexity, and interpretability of results. This paper addresses these challenges and provides a comprehensive review of the most widely used data mining methods in financial engineering. Furthermore, it discusses the potential for future advancements in data mining algorithms and their integration with other emerging technologies like artificial intelligence and big data analytics. The findings indicate that while data mining offers significant potential in financial analytics, careful consideration must be given to the selection of appropriate techniques and the proper handling of data to ensure reliable predictions. Ultimately, this paper aims to contribute to the ongoing development of robust predictive modeling techniques in financial engineering by emphasizing the importance of data mining as an indispensable tool.

This chapter provides insight into various areas within the medical field that strive to take advantage of different data mining techniques in order to realize the full potential of their knowledge assets. Specifically, this is done by discussing many of the limitations associated with conventional methods of diagnosis and showing how data mining can be used to improve these methods. Comparative analyses of different techniques associated with various areas within the medical field are outlined in order to identify the right technique for particular medical specialties. Furthermore, suggestions are provided to appropriately utilize the various data mining techniques thereby leading to effective and efficient knowledge management and knowledge utilization. In this chapter we highlight the potential of data mining in improving the exploratory as well as the predictive capabilities of conventional diagnostic methods in medical science.

Our research transacts with a great various audiology data from National Health System (NHS) facility, including audiograms, structured data such as age, gender, and diagnosis, and a text of specific information about each patient, i.e., clinical reports. This research examines factors related to audiology patients depends on various data by using the mining and analysis of this data. This paper looks for factors affecting the choice between two prevalent hearing aid kinds: BTE (Behind The Ear) or ITE (In The Ear). This choice often done by audiology technicians working in specific clinics for this purpose, based on audiograms results and patient consultation. In many situations, there is an obvious choice, but sometimes the technicians need for the second opinion via an automatic system includes clarification of how to obtain that second opinion. The research deals with diversified specifics and more significant factors for choosing of confirmed hearing aid related to those specifics. We depend on the earlier study data (Bareiss, E. Ray, & Porter, Bruce (1987)). Protos: An Exemplar-Based Learning Apprentice. In the Proceedings of the 4th International Workshop on Machine Learning, 12-23, Irvine, California, which illustrates the database analysis for 180,000 records, for 23,000 patients, by the hearing aid clinic at James Cook University Hospital in Middlesbrough, UK. This data mined to find which factors contribute to the deduction to fit a BTE hearing aid as opposed to an ITE hearing aid. Here we conduct some enhancements on this database and analyze the data depends on medical information to create a new class then we use some intelligent Data Mining (DM) techniques to guess the most correct illness that could be associated with patient's information. Based on the result (according to the patients' diagnosis details), we can obtain right predictions of which type of Hearing Aid (HA) they should use.

Breast cancer is the most common invasive cancer in females worldwide. Breast cancer diagnosis and breast cancer prognosis are the two important challenges for the researchers in the medical field and also for the practitioners. If the cells in the breast start to grow without any control, it leads to cancer. Normally, the growth of the lump can be seen using x-ray. The benign and malignant breast lumps are distinguished during breast cancer diagnosis. The prognosis process predicts the period at which the breast cancer is likely to reappear in patients who have had their cancers removed. Data mining techniques and machine learning algorithms are mostly used in the whole process of breast cancer diagnosis and treatment. They utilize the large volume of breast cancer data for extracting knowledge. The application of data mining and machine learning methods in biomedical research is presently vital and crucial in efforts to transform intelligently all available data into valuable knowledge.

Controlled clinical trials are studies done by scientists on a concrete domain with the objective of execute a prospective analysis that attempts to compare the effect and value of one or more interventions in humans under specific conditions. Our case study is focused on applying artificial intelligence techniques to support scientists working on bio-nutritional trials, which rely on the study of the effect of consuming food with biomarkers on human health. This work is developed under the HENUFOOD2project. In order to simplify the domain, two trial stages are considered: the trial experimental execution and the trial data analysis. It is after the trial has been executed and all the information collected, when we try to discover non obvious knowledge among the trial data and support the scientists on their everyday analytical tasks. On the one hand, this analysis includes generic statistical manipulation and data discovery empowered by data mining techniques. On the other hand, a scientist support engine has been developed in order to allow non data manipulation and analysis experts to access the information and ask for new analysis processes. The approach, thanks to a user friendly interface enabled with query answering techniques, is able to interact with the user in a conversational mode and run analytical or data discovery processes, do a semantic search on previous indexed works or, finally, infer new rules to feed the expert system ontology. The expert system is not fully covered on this paper, as it will focus on the explanation of the usage of data mining and query answering techniques on that bio-nutritional focused expert system.

An immune-inspired semi-supervised algorithm for breast cancer diagnosis

Diabetes is one of the major health issues among youngsters nowadays because of poor diet, family history, lifestyle habits. The medical diagnosis is important based on complicated risks that should be performed accurately and effectively. Based on the result and reports further investigation is needed through diagnosis and treatment are given to the patient. Many numbers of the data can be available in the healthcare systems. The availability and necessary medical data can be analyzed for data analysis tools to extract useful information through pattern recognition. Both data mining and knowledge discovery data are infinite applications including business and research scientific fields. Diabetes is one of the major applications nowadays where data mining tools are very useful to easily diagnose and find the solution. This paper, diagnoses diabetes through data mining tools such as SVM, association rule, clustering, and association. So, Data mining helps to predict and diagnose diseases with a low occurrence of risks. In this paper, the main focus is to make a present detailed survey of various data mining techniques and approaches that have been put to use for the prognosis of diabetes.

Chapter 12 - Application, functionality, and security issues of data mining techniques in healthcare informatics

Intelligent computing system based on pattern recognition and data mining algorithms