A review of Medical Diagnostics Via Data Mining Techniques (original) (raw)

Review of medical diagnostics via data mining techniques

Iraqi Journal of Science, 2021

Data mining is one of the most popular analysis methods in medical research. It involves finding patterns and correlations in previously unknown datasets. Data mining encompasses various areas of biomedical research, including data collection, clinical decision support, illness or safety monitoring, public health and inquiry research. Health analytics frequently uses computational methods for data mining, such as clustering, classification, and regression. Studies of large numbers of diverse heterogeneous documents, including biological and electronic information, provided medical and health studies.

The Application of Data Mining Methods for the Process of Diagnosing Diseases

2019

Today, the medical field has a large amount of medical information that requires proper processing and further use in the diagnosis and treatment of various diseases. The development of computer technology provides tremendous opportunities for collecting, processing, managing and researching medical information to better understand the complex biological processes of life and help solve the problem of diagnosis and treatment in medical institutions. Accurate diagnosis and proper treatment provided to patients is one of the main tasks of medical care. Therefore, data mining techniques, which are part of knowledge discovery in databases, are becoming popular tools for medical researchers. The use of these tools makes it possible to identify and use patterns and relationships between numerous variables and to predict specific disease and treatment outcomes. This paper describes the application of data mining methods for the process of diagnosing diseases. To improve the effectiveness o...

The impact of data mining techniques on medical diagnostics

Data Science Journal, 2006

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. However, the available raw medical data are widely distributed, heterogeneous in nature, and voluminous. These data need to be collected in an organized form. This collected data can be then integrated to form a hospital information system. Data mining technology provides a user-oriented approach to novel and hidden patterns in the data. Data mining and statistics both strive towards discovering patterns and structures in data. Statistics deals with heterogeneous numbers only, whereas data mining deals with heterogeneous fields. We identify a few areas of healthcare where these techniques can be applied to healthcare databases for knowledge discovery. In this paper we briefly examine the impact of data mining techniques, including artificial neural networks, on medical diagnostics.

Data Mining Algorithms And Medical Sciences

Extensive amounts of data stored in medical databases require the development of dedicated tools for accessing the data, data analysis, knowledge discovery, and effective use of sloretl knowledge and data. Widespread use of medical information systems and explosive enlargement of medical databases require conventional manual data analysis to be coupled with methods for competent computer-assisted analysis. In this paper, I use Data Mining techniques for the data analysis, data accessing and knowledge discovery procedure to show experimentally and practically that how consistent, able and fast are these techniques for the study in the particular field? A solid mathematical threshold (0 to 1) is set to analyze the data. The obtained outcome will be tested by applying the approach to the databases, data warehouses and any data storage of different sizes with different entry values. The results shaped will be of different level from short to the largest sets of tuple. By this, we may take the results formed for different use e.g. Patient investigation, frequency of different disease.

Data Mining Techniques for Physicians to Diagnose Diseases

This paper covers the vital problems associated with the data mining method and its usage within the medical field. To achieve that objective we've got to live and take a look at the applicability of knowledge mining in predicting diagnoses and in discovering vital relationships between diagnoses and therefore the different information attributes. This has been done through the cases of kidney failure and hurting diseases. Preparation of knowledge included choosing, cleansing, modeling and analysis. It was proved that the info mining is applicable within the medical sector and can improve the various medical applications

General Framework for Biomedical Knowledge With Data Mining Techniques

2013

Data mining is the process which automates the extraction of predictive information discovers the interesting knowledge from large amounts of data stored in information repositories. Biomedical informatics (BMI) is the science underlying acquisition, maintenance, retrieval, collecting, manipulating, and analysing the biomedical knowledge and information to improve medical data analysis, problem solving, and decision making, inspired by efforts toward progress in medical domain. In this research work a comprehensive framework will be generated which comprises of various data mining techniques and evaluate meaningful information from biomedical data. Data mining field will be applied to biomedical data to analyze the characteristics, identify patterns of interest, for diagnosing and predicting patients' health. These proposed biomedical data mining framework useful to the scholars who are interested in the related researches of data mining and medical domain. Data mining is a repl...

Data Mining: A Novel Outlook to Explore Knowledge in Health and Medical Sciences

2014

Today medical and Healthcare industry generate loads of diverse data about patients, disease diagnosis, prognosis, management, hospitals’ resources, electronic patient health records, medical devices and etc. Using the most efficient processing and analyzing method for knowledge extraction is a key point to cost-saving in clinical decision making. Data mining, sometimes called data or knowledge discovery, is the process of analyzing data from different perspectives and summarizing it into useful information. In medicine, this process is distinct from that in other fields, because of heterogeneity and voluminosity of the data. Herein we reviewed some of published articles about application of data mining in several fields in medicine and healthcare.

Applying Data Mining Techniques for Predicting Diseases

Int. J. Advanced Networking and Applications , 2019

The techniques of data mining are very popular of Diseases. The advancement in health analysis has been improved by technical advances in computation, automation and data mining. Nowadays, data mining is getting used in a vast area .The Nature of the medical field is made with the knowledge wherever there's a spread of data but untapped during a correct. and thus, the foremost serious challenge facing this area is the quality of service provided which suggests to create the diagnose during a correct manner in a timely manner and supply acceptable medications to patients. Thus Health information technology has emerged as a replacement technology within the health care sector in a short amount by utilizing Business Intelligence 'BI' that could be a data-driven Decision Support System. The various techniques of data mining are used and compared during this analysis.

Clinical data mining: a review

2009

Objective: Clinical data mining is the application of data mining techniques using clinical data. We review the literature in order to provide a general overview by identifying the status-of-practice and the challenges ahead. Methods: The nine data mining steps proposed by Fayyad in 1996 [4] were used as the main themes of the review. MEDLINE was used as primary source and 84 papers were retained based on our inclusion criteria. Results: Clinical data mining has three objectives: understanding the clinical data, assist healthcare professionals, and develop a data analysis methodology suitable for medical data. Classification is the most frequently used data mining function with a predominance of the implementation of Bayesian classifiers, neural networks, and SVMs (Support Vector Machines). A myriad of quantitative performance measures were proposed with a predominance of accuracy, sensitivity, specificity, and ROC curves. The latter are usually associated with qualitative evaluation. Conclusion: Clinical data mining respects its commitment to extracting new and previously unknown knowledge from clinical databases. More efforts are still needed to obtain a wider acceptance from the healthcare professionals and for generalization of the knowledge and reproducibility of its extraction process: better description of variables, systematic report of algorithm parameters including the method to obtain them, use of easy-to-understand models and comparisons of the efficiency of clinical data mining with traditional statistical analyses. More and more data will be available for data miners and they have to develop new methodologies and infrastructures to analyze the increasingly complex medical data.

Contemporary Affirmation of the Recent Literature on Disease Prediction Using Data Mining Techniques

Data Mining is used comfortably in a constructive way in areas such as e-business, marketing and retail. Due to this factor it is now relevant in knowledge discovery in databases (KDD) in economy and industrial areas. Fields like medicine and public heath are two areas where data mining is getting popular immensely. This report discusses the application of methods involved in data mining in healthcare and disease diagnosis. The complexities involved in using data mining in healthcare are also touched upon. Surveys conducted on data mining and healthcare state that the use of former has increased. It helps in making good health policy, knowing the disease, protecting death and illegal insurance claims.