Chapter 26 - Relevance of data mining techniques in real life

Educational Data Mining (EDM) is an evolving field with a suite of computational and psychological methods for understanding how students learn. Applying Data Mining methods to education data help us to resolve educational investigation issues. The growth of education data offers some unique advantages as well as some new challenges for education study. Some of the challenges are an improvement of student models, identify domain structure model, pedagogical support and extend educational theories. The main objective of this paper is to present the capabilities of data mining in the context of the higher educational system and their applications and progress, through a survey of literature and the classification of articles. We observed the works on investigational situation studies showed in the EDM during the recent past, in addition, we have introduced three data models based on descriptive and predictive data mining techniques. This is oriented towards students in order to recommend learners’ activities, resources, suggest path pruning and shortening or simply links that would favor and improve their learning or to educators in order to get more objective feedback for instruction.

In the field of weather, forecasting is a critical application that has long been considered one of the world’s most difficult scientific and technical issues. Data mining is as the practice of discovering previously unknown and practical information and knowledge that is embedded in enormous volumes of unclear, noisy, random, and incomplete data. In this study, however, an examination of many common data mining techniques for weather prediction has been offered. Different type of data mining techniques has been investigate. Also, based on that, weather prediction comparison using different data mining approaches has been investigated. Nevertheless, methodologies like data cleaning, data integration, data selection, data transformation, etc. has been discussed for reaching the best solution of predicting weather by using data mining. Finally, this study illustrates how k-means and decision trees are the most appropriate data mining methods for this use case.

Prediction of post-COVID chronic fatigue syndrome using data mining and machine learning techniques in Isfahan COVID cohort study.

The healthcare industry has witnessed a huge evolution in producing enormous amount of medical data that gave birth to the research in multiple fields. Efforts were done and are being done by the researchers in exploring the medical data in order to extract useful knowledge by applying the technical innovations. Electronic Health Record systems (EHRs) are the repositories of data which is the digitized format of the storing the medical data. Electronic health data is the fastest growing data diversity. Technological advancements in health care information, digitalizing health records has resulted in rapid growth of healthcare sector. Healthcare sector manages huge amount of data and need to analyse the data available and provides a better solution which makes better decision. One of the challenges is how to discover useful and important information effectively among the massive amount of data available through the data mining techniques. Data Mining plays a major role in advancing and developing the new techniques which works effectively for the big data available in healthcare. This paper reviews different predictive and descriptive data mining techniques that had been applied to voluminous health record in aid of decision making process.

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

This paper brings in an insight of the number of ways the students’ performance can be predicted. The performance of a student plays a vital role in any institution. It is the measure for assessing the academic excellence of any esteemed educational body. Not only the institute but the academic performance matters to the student as well. There is a tough competition in the field of academics. Students spend their sleepless nights turning up the pages of their books. Well, amidst of this, a student tussle with many hardships. At present, there are various methodologies to predict the student's performance and Data mining provides the most scientific and dependable method for predicting the same. Educational data mining is the process behind the evaluation of useful patterns out of huge education databases. These patterns help the educators in predicting the performance of the student. In a way it also helps in advancing effective teaching. Data mining techniques can be useful in culling out the data concerted to specific needs. This paper presents the study to find out the gap in the existing performance prediction methodologies.

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 is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

Diseases chronic, including heart disease, cancer, diabetes, and obesity, are the main causes of mortality in the United States and accounting for and consuming the majority of the country's healthcare expenditure. As indicated by recent researches. The main reason for the emergence of these diseases prominently is their relationship to each other, where diabetes is one of the causes of cancer and heart disease, hepatitis also is associated with diabetes, and heart disease. This paper focuses on data mining and machine learning techniques in healthcare classification and prediction of diseases and rebuild disease detection systems (DDS). The study suggests finding a classifier among the most common kinds of classification algorithms within a combined approach represent in Bayesian, Trees, Rules, Function, and lazy algorithms to automate a better performance of early detection of diseases from the medical datasets. This paper presents and analyzes five different machine learning (ML) algorithms: Function-based Neural Network (MLP) algorithm. Trees based Decision Tree (ID3) algorithm, Bayesian Theorem based Hidden Naïve Bayes (HNB) algorithm. Lazy based k-nearest neighbors (IBK) algorithm, and Rules-based OneR algorithm. The analysis is based on four benchmark datasets in the healthcare sector, including the Pima Indian Diabetes PID, the Breast Cancer, Heart Cleveland, and Hepatitis Datasets, which were obtained from several ML repositories. The results show that the HNB predicts the best result with a relatively higher Precision, AUROC Statistic, highest accuracy, and performance when compared to MLP, IBK, OneR, ID3 algorithms.

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.

Knowledge discovery in cardiology: A systematic literature review

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

Educational data mining: A survey from 1995 to 2005

Association rules provide an important clue on how hidden knowledge and patterns can be extracted and the attributes extracted are associated when big data is mined by means of a particular data mining algorithm. The context of HEIs, where big data mining is uncommon, there is a need to know how association rules could be utilized to discover hidden knowledge and forecast student performance based on time-to-degree and cumulative grade point average (CGPA) and also make decisions. This research has demonstrated this by taking the example of the dataset of 337 graduated students of a university in Bahrain belonging to the Bachelor's degree in Accounting and Finance. Two types of data mining concepts were applied, regression and a priori algorithm. Regression results showed that there is a statistically substantial link between time-to-degree and course-difficulty and not CGPA. Similarly, association rules showed the link between time-to-degree, course-taking pattern, no. of courses and course-difficulty pattern but not CGPA. The association rules generated provided the basis to predict optimum time-to-degree by controlling the no. of courses, course-taking pattern, and course-taking difficulty. The findings of this research provide an important window of opportunity for HEIs to enhance student performance using data mining techniques. This investigation has contributed theoretically showing the application of data mining theory using apriori algorithm and regression, for HEI big data mining where student factors and organizational factors are said to be fairly different from that of the HEIs where research on big data is seen to be a well-known activity.

The most fatal disease on the earth is thought to be illness of the heart. There are a lot of features that change the heart’s composition or functionality. In most cases, it is hard for doctors to make a diagnosis accurately and quickly. This study’s objective is to determine critical factors and methods of data mining which can improve the accuracy for prediction of heart disease. Further, it is essential to make use of automatic technologies in diagnosing heart diseases as early as possible. To develop a new prediction technique for heart disease that comprises four phases such as “(a) Pre-processing, (b) Feature extraction, (c) Feature selection and (d) Classification”. The initial stage of pre-processing is when the incoming data is treated to the elimination of redundant and missed numbers. Then, from the initial stage of data, the higher-order statistical and statistical characteristics, chi-squared features and symmetrical uncertainty attributes are derived. However, when working with a greater number of characteristics, the curse of dimensionality was a severe issue. Hence, the characteristics of optimal features are planned from the overall set of features. A novel Hybrid Bull and Elephant Algorithm (HB-EA) is introduced for the selection of optimal features. Consequently, the selected set of features is subjected to various classifiers as an ensemble model that contains “Naïve Bayes (NB), Decision Tree (DT), Neural Network (NN), Support Vector Machine (SVM), Optimized Recurrent Neural Network (RNN) and Linear Regression (LR)”. The final step is to log off efficiency for outputs obtained from the group of classifiers and determine the outcome. The RNN weights are ideally tuned by the suggested HB-EA technique to boost the system’s accuracy. The proposed model is finally evaluated against existing techniques to determine its superiority. The suggested technique for dataset 1 achieved maximum accuracy (0.916), and it is 15.24%, 8.76%, 7.56%, 4.09% and 1.89% better than convolution schemes like Random Forest (RF), Deep Belief Network (DBN), SVM, [Formula: see text]-Nearest Neighbor (KNN) and Elephant Herding Optimization (EHO) models.