Role Of Data Mining Techniques Research Articles

The role of data mining in diagnosing the diseases: A case study of detecting the thyroid disease

One of the most important tools used in the field of medicine is to search for data, especially in the field of exploration and knowledge of the prevailing health conditions, research into diseases and patient behavior in society, as well as contribute to knowing the effect of drugs and drugs on patients according to previous patient records. Data mining also helps to identify diseases spread in a specific area, which helps to take the necessary measures and awareness to control this disease, and works to develop and update the field of medicine and medicines and increase their spread. Efficiency and processing capacity. Data mining techniques are a recent method in the medical field, and have become increasingly reliable in diagnosing diseases especially in diagnosing and detecting thyroid diseases. Since such techniques help to properly analyze and accurately predict the disease, they contribute to helping physicians, providing appropriate medical care, and reducing the incidence or development of side effects of the disease. Today, data mining techniques are important in the medical field as they help detect diseases and epidemics that are spreading around the world. The purpose of this paper is to know the role of data mining techniques in the diagnosis of thyroid disease through the survey of a number of relevant studies. The critical appraisal tool of previous studies in this area has been used. The study has also found the importance of early diagnosis of thyroid diseases, providing proper treatment for patients. It also agreed on the importance of data mining tools such as neural networks, Machine and decision tree learning and their disease diagnosis potential. The paper recommends the need for extensive systematic reviews of studies on the use of data mining techniques in the diagnosis of thyroid diseases. Also, we concluded that Prospecting tools in medical devices have had an enormous and important impact on the health care industry and the country. It should be remembered that the medical data that began to multiply in a large amount must be contained herein a lot of useful information that greatly affects the improvement of the level of medical services and detection in Characteristics of many diseases and epidemics and find solutions to many difficult diseases.

On the adaption of data mining technology to categorize cancer diseases

Along with data mining, tools and software have emerged to aid in mining the vast and growing amount of data to access knowledge in databases. These tools facilitate work on most scientific disciplines, including sciences, Libraries and information. Accordingly, Data mining became an effective technique for obtaining knowledge to achieve the basic goal of discovering hidden facts that are contained in databases through the use of multiple technologies that include artificial intelligence, statistical analyzes, techniques and data modeling etc. Medical data mining is considered one of the most important tools used in the field of medicine, especially in exploring and knowing health conditions according to records of former patients. In addition, data mining helps not only in categorizing cancer but also in taking the necessary measures. With the spread of cancer at high rates around the world, the need to develop smart methods that have the ability to predict the disease appeared. Applications of data mining techniques spread as human attempts to control this deadly disease, with the aim of awareness, early detection and reduction of treatment costs. This prompted the researcher's curiosity to know the ability of data mining to categorize cancer. This work aims at reviewing ways to solve one of the problems that doctors suffer from, which is the problem of diagnosing diseases that lead to death, including cancer, as there is huge information that has not been used. Therefore, this work tries to solve this problem using data mining technology in addition to helping doctors make the right decision. The study reached several conclusions, namely the fact that the studies presented in the paper demonstrated the effective role of data mining techniques in reducing medical errors in terms of their ability to predict and accurately diagnose the disease, as well as the effectiveness of the algorithms of the data mining technique in predicting the presence of the disease at an early stage. Thus, we found that the clinical field needs to expand research, foster new kinds of calculations, and apply them practically speaking to create the best and most precise outcomes and even to supplant or surpass specialists' performance at this level..

The role of data mining techniques and tools in big data management in healthcare field

Data mining is one of the most important modern techniques used to achieve high output standards at all levels. The twenty-first century saw the advent of a new trend to improve medical services in the healthcare sector. To bridge the gap between previous studies and the practical applications of data mining, this study aimed to review the theoretical literature and previous studies related to the demonstration of data mining techniques and tools and their role in big data management. To achieve the objectives of the study, the researchers used a descriptive, analytical, documentary method. The study concluded many results including that in the era of the knowledge and technology revolution, data mining is one of the important issues, that requires everyone to take into account its achievements in our current era, as well as the existence of a correlation between big data and the provision of a separate health service in the field of healthcare, and work to address epidemics and discover vaccines for them. In the healthcare industry, data mining plays a vital role, especially in predicting various types of diseases. In detecting diseases, diagnosis is the main tool. The study recommended the need to conduct more experimental and exploratory studies dealing with healthcare data mining techniques and tools and their effect on the management of big data volumes, especially in our Arab countries and the need for the development of models and action plans and the development of processes and methods from which data in the healthcare sector can be explored.

Role of data mining techniques in business

Objective: The main objective is to elaborate and discuss different techniques used in data mining, to analyze different strategies of data mining to make improvements, and to find more powerful mining techniques for the betterment of the business. Methods: Multiple techniques and strategies of data mining are used to improve the business. We employed the data warehouse methods for the improvements of the business using Business Intelligence (BI) and Business Analytics (BA) along with their types and instruments. We are also discussing some tools used for data mining or ordering organizational data. Findings: We employed Business Intelligence-(BI) and Business Analytics-(BA) techniques for the improvement of the business. Earlier, there were only four (Regression, Classification, Association, and Clustering) techniques that were used for business improvements. It is found that Crawler is the best tool for BI or BA data mining. Novelty : This study analyzed that, BI and BA are the best ways used for data mining, data ordering, or format of data in business. Earlier, these ways were not in use for data mining. Data mining may be the best approach to improve the business. Keywords: Business Intelligence (BI); Business Analytics Data Mining (BADM); Data Warehouse (DW); Knowledge Discovery (KDD) Customer Relationship Management (CRM); Enterprise Resource Planning (ERP)

Role of Data Mining Techniques in Bioinformatics

Data mining offers a highly effective technique that is useful in research and development of bioinformatics. Bioinformatics consists biological information such as DNA, RNA, and protein. Data mining tasks/techniques are classification, prediction, clustering, association, outlier detection, regression, and pattern tracking. Data mining provides important correlation, hidden patterns, and knowledge from the bioinformatics data set. This paper presents the role of data mining techniques in bioinformatics application. Classification of gene and protein structure, analyzing the gene expression, association of co-disease, outlier detection and gene selection, protein structure prediction, and drug discovery are some typical biological example that has proven data mining as a suitable technique for bioinformatics.

Geriatric depression symptoms coexisting with cognitive decline: A comparison of classification methodologies

Geriatric depression is a pathological process that causes a great number of symptoms resulting in limited mental and physical functionality. The computation of oscillatory and synchronization patterns in certain brain areas may facilitate the early and robust identification of depressive symptoms. In this study electroencephalographic (EEG) data were recorded from 34 participants suffering from both cognitive impairment and geriatric depression (mean age 69.81) and 32 control subjects (mean age 70.33). Both groups were matched according to their cognitive status. The study aims at evaluating neurophysiological features of elderly participants suffering from depression and neurodegeneration. The current work focuses on the identification of depression symptoms that coexist with cognitive decline, the correlation of the examined neurophysiological features with geriatric depression combined with cognitive impairment and the investigation of the role of data mining techniques in the analysis of EEG data. The EEG features were estimated through synchronization analysis (Orthogonal Discrete Wavelet Transform). Depressive patterns were detected through data mining techniques. Random Forest, Random Tree, Multilayer Perceptron (MPL Network) and Support Vector Machines (SVM) were employed for data classification. The efficiency of the classifiers varied from 92.42 to 95.45%. Random Forest demonstrated the highest accuracy (95.5%). Both synchronization and oscillatory features contributed to the decision trees’ formation, with the former prevailing. Moreover, synchronization features significantly contributed to the decision trees’ formation. In line with previous neuroscientific findings, synchronization among right frontal–midline anteriofrontal regions showed great correlation with depressive symptoms. Evaluation of the classifiers indicated the Random Forest as being the most robust algorithm. Synchronization of certain brain regions is more indicative of identifying depression symptoms than oscillatory since synchronization features contributed the most to the formation of the classification trees.

Data Mining : A Prospective Approach for Digital Forensics

Data mining is part of the interdisciplinary field of knowledge discovery in databases. Research on data mining began in the 1980s and grew rapidly in the 1990s.Specific techniques that have been developed within disciplines such as artificial intelligence, machine learning and pattern recognition have been successfully employed in data mining. Data mining has been successfully introduced in many different fields. An important application area for data mining techniques is the World Wide Web Recently, data mining techniques have also being applied to the field of criminal forensics nothing but Digital forensics. Examples include detecting deceptive criminal identities, identifying groups of criminals who are engaging in various illegal activities and many more. Data mining techniques typically aim to produce insight from large volumes of data. Digital forensics is a sophisticated and cutting edge area of breakthrough research. Canvass of digital forensic investigation and application is growing at a rapid rate with mammoth digitization of an information economy. Law enforcement and military organizations have heavy reliance on digital forensic today. As information age is revolutionizing at a speed inconceivable and information being stored in digital form, the need for accurate intellectual interception, timely retrieval, and nearly zero fault processing of digital data is crux of the issue. This research paper will focus on role of data mining techniques for digital forensics. It also identifies how Data mining techniques can be applicable in the field of digital forensics that will enable forensic investigator to reach the first step in effective prosecution, namely charge-sheeting of digital crime cases.

Data mining’s capabilities for knowledge creation in the GCC counties

Purpose: This paper highlights the importance of using data mining in sustaining development in GCC countries by showing the abilities of data mining techniques of enhancing and improving the discovered knowledge. Design/methodology/approach: This paper studies the role of data mining techniques to support the creation of scientific capacity to reduce digital divide and accelerate the process of sustaining development. This will help making predictions about future trends using both historical and current data and allowing decision makers and policy makers to make effective knowledge based policies. Findings: The paper is concluded with the impact of data mining techniques on future development in GCC countries which have been proved based on the cur- rent potential for knowledge creation driven by data mining in the different arenas.