A Research Study On Data Mining

1. A Clinical Support System is a system that aids physicians and clinical staff in finding the right course of action for patient care. The system recommends the action and also explains why that route was chosen for that specific patient. An example of a clinical support system is one that assists with the retrieval of scientific medical information as well as takes into consideration the particular clinical context. The system does not perform the clinical decision making but it provides relevant information, analyses and recommendations about the specific patient, this helps the healthcare provider make an educated decision about the care that needs to be provided.

Accurate data mining works wonders for medical billing agencies to improve their level of service and collection. With new and improved innovations being incorporated in medical billing software, unique reports can be executed that help in providing specific information which then assists in decision making and implementing improvements.

Data mining is defined as the process of data selection and studying and building models using massive data stores to disclose previously unidentified patterns in databases (Koh and Tan, 2005, p. 64). Koh and Tan have found financial institutions, marketers, manufactures and so has many other agencies have used data mining. Data mining has been of great use by various organizations. For example, data mining has been useful to detect fraudulent credit card transactions (Koh and Tan, 2005, p. 64). Koh and Tan stated, “In healthcare, data mining is becoming increasingly popular, if not increasingly essential” (Koh and Tan, 2005, p.64). In healthcare there have been reports that data mining has been successful in detecting fraud and abuse in healthcare claim (Koh and Tan, 2005, p.65). There are many factors in healthcare that have driven the use of data mining applications. One of the factors that have driven healthcare to use the data mining applications will be the medical insurance fraud and abuse. All organizations currently involved in the healthcare industry can profit from the data mining applications. For example, data mining is able to help

Every day the surgical interventions are associated with medicine, and the area of critical care medicine is no exception. The goal of this work is to assist health professionals in predicting these interventions. Thus, when the Data Mining techniques are well applied it is possible, with the help of medical knowledge, to predict whether a particular patient should or not should be re-operated upon the same problem. In this study, some aspects, such as heart disease and age, and some data classes were built to improve the models created. In addition, several scenarios were created, with the objective can predict the resurgery patients. Regarding the primary objective, the resurgery patients’ prediction, the most important metric is the sensitivity,

Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical

Sequential patterns are a new application that has been proposed to help doctors and diabetic patients. There is one main advantage to the sequential patterns. The article says the advantage is the transparency of the model. These patterns can be easily understood by physicians. The sequential patterns were used to tie information together and make patterns between different hospitals. Mining diabetes data is used with sequential patterns. The patterns

In healthcare organization data mining plays the most leading role in the research area. Data mining plays a vital role in various fields of technology. In healthcare industry we gather more information regarding patients, diseases, hospital resource, electronic patient’s records, diagnosis methods, etc., by using health care in data mining it is easy to classify or group the patients having the same disease so that it helps to treat them effectively. In this paper I have reviewed about data mining application in health care and data mining challenges in health care.

But due to partially complete medical data sets, accuracy often decreases, results in more number of misclassification that can lead to harmful complications. An accurate prediction and diagnostic of a disease becomes a challenging research problem for many researchers. Therefore, aimed to improve the diagnosis accuracy we have proposed a new methodology, based on novel preprocessing techniques, and K-nearest neighbor classifier. The effectiveness of proposed methodology is validated with the help of various quantitative metrics and a comparative analysis, with previous reported studies using the same UCI dataset focusing on pimadiabetes disease diagnosis.

Data mining as it has been established is simply a way of getting hidden knowledge or information from data, the technique(s) employed search for concurrency, relationships, and outliers in this data that they present as knowledge [4]. This knowledge can then be used in different applications. Prediction is one of the ways that the hidden knowledge gotten from the data can be used. The main aim is to use a large number of past values to consider probable future [9]. Heart disease is the common term for a variety of diseases, conditions, and disorders that affect the heart and the blood vessels [6]. Symptoms of heart disease vary depending on the type of heart disease. Congenital heart disease suggests to a problem with the heart 's structure and function due to abnormal heart development before birth. Congestive heart failure is when the heart does not pump adequate blood to the other organs in the body [8]. Coronary heart disease or in its medical term Ischemic heart disease is the most frequent type of heart problem [24]. Coronary heart disease is a term that refers to damage to the heart that happens because its blood supply is decreased; it leads to fatty deposits build up on the linings of the blood vessels that provide the heart muscles with blood, resulting in heart failure. Heart disease prediction using data mining is one of the most interesting and challenging tasks. Shortage of specialists and high wrongly diagnosed

Question 1: Assume a base cuboid of 10 dimensions contains only three base cells: (1) (a1, b2, c3, d4; ..., d9, d10), (2) (a1, c2, b3, d4, ..., d9, d10), and (3) (b1, c2, b3, d4, ..., d9, d10), where a_i != b_i, b_i != c_i, etc. The measure of the cube is count. 1, How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2, How many nonempty aggregate (i.e., non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27 while the number of cells overlapping once is 4 ∗ 27 . So the final calculation is 3 ∗ 210 − 2 ∗ 27 − 1 ∗ 4 ∗ 27 − 3, which yields the result. 3, How many

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

little or no control over the data gathering process, with data often being collected for some

Machine learning has been gaining popularity in healthcare because of its ability to use existing mathematical models and apply them to new instances of an established concept in other data. This ability to automatically identify patterns in data is one of the major reasons for the potential of machine learning in healthcare settings—as well as its ability to fill in the gaps of expert knowledge, adjust for exceptions, and efficiently handle massive amounts of data [1].

The qualitative research for the study will be driven by using tools such as excel spreadsheets and data analysis software that provides a quality control and accuracy for the results obtained from the participants. The researcher will be utilizing two elements of data analysis validity according to Creswell described by Sampson, J (2012), congruence and accuracy:

Mining valuable patterns in different data streams have been a significant research area in data mining research during the last decade. There are several proposed techniques for data mining that have been developed for mining patterns from different text documents. But to determine the method in which the patterns are discovered effectively is a popular issue in data mining research including text mining area. Most of the popular methods in text mining make use of term-based methodology which involves problems like synonym and polysemy. Some research on text mining proves that the pattern based or phrase based approach performs better compared to the term-based approach but there is no concrete evidence to prove this point. The