Data mining is the technique to interpret the data from other perspective and summarize the data so that the data can be useful information. Technically, data mining is a process to identify relations or patterns in the databases to predict the likelihood of future events. According to Eliason et al, there are three systems for healthcare organization to implement the mining data systems. The three systems are the analytics system, the content system and the deployment system. The analytics system is a system that used to collect all data such as patients clinical data, patients financial data, patients satisfactory data and other data. The content system is used to store all medical evidenced data. The deployment system is used to make new organization structure. There are several elements that consist in data mining which are first extract, transform and load transaction data onto the data warehouse system, second, store and manage the data in a multidimensional system, third, provide data access to information technology professionals, forth, analyze the data by application software and lastly, present the data in graph or table format. Data mining technique can be used to overcome many health disorders. The health diseases that can be overcome are heart diseases. Palaniappan S. & Awang R. stated data mining can be done to extract all information that associated with heart disease from the database. Through this technique, this will help the physician or health practitioner to make clinical interpretation about the heart disease and help in giving a good treatment for lower cost. According to Shouman M. et al, data mining is important for determine the prognosis of the heart disease and the technique that can be used such as t... ... middle of paper ... ...omote awareness and prevention among people. In addition, according to Marinov M. et al, the data mining of the diabetes shown that very useful to analyze the diabetes-related health care flow, and adverse drug effects, processing or cleaning the diabetes-related data for mining, detect diabetes-related health care fraud of insurance claims, enriching diabetes-related clinical guidelines by incorporate the new guidelines, or for detect signs of early mortality. As a conclusion, the data mining can be used in various data that are collected from the healthcare organization when perform their daily activities. These data are refer as process data, as all the information is collect from daily administrative information such as the cost claim, physicians workload, patients hospital administrative and from laboratory data or result and as well as the drug prescription.
Diabetes education is a structured education and self-management (at diagnosis and regularly reviewed and reinforced) to promote awareness. Diet and lifestyle, healthy diet, weight loss if the person is overweight, smoking cessation, regular physical exercise. Maximizing glucose control while minimizing adverse effects of treatment such as hypoglycemia. Reduction of other risk factors for complications of diabetes, including the early detection and management of hypertension, drug treatment to modify lipid levels and consideration of antiplatelet therapy with aspirin. Early intervention for complications of diabetes,, including cardiovascular disease, feet problems, eye problems, kidney problems and neuropathy.
Diabetes is a disease that is very common in the world. Early detection of diabetes can significantly decrease the risk of it getting worse throughout a person’s life. There are symptom...
The use of data mining software, which enables electronic learning or information innovation in databases, provides a different viewpoint: There is also the possibility that a patient’s information that should be safeguarded , could be revealed without information instantly being recognized as such (Goodman & Cava, 2008). Therefore, the question of exploring the privacy challenges SAHC leaders ’ experience as a result of new technology is significant; a challenge this study is focused to
Diabetes is a common disease, which can be a serious, life-long illness caused by high levels of glucose in the blood. This condition is when the body cannot produce insulin or lack of insulin production from the beta cells in the islet of Langerhans in the pancreas. Diabetes can cause other health problems over time. Eye, kidneys, and nerves can get damaged and chances of stroke are always high. Because of the serious complications, the purposes of teaching a plan for diabetes patients are to optimize blood glucose control, optimize quality of life, and prevent chronic and potentially life-threatening complications.
The aim of this assignment is to create a decision support system for a clinical cases.
Data mining is also called knowledge discovery in database. In computer science, the process of discovering knowledge and relationship in large amount of data. This field combines from statistics and artificial intelligence with database management known as data set.
In the past, the term Data Mining was, and still is, used to designate the activity of pulling useful information from databases. Now, this term is recognized to apply but to one activity in a very large process to extract knowledge from opaque databases. The overall process is known as Knowledge Discovery in Databases, (KDD). This process is comprised of many subprocesses which when linked together provide a firm foundation for knowledge acquisition from large databases. Many tools, techniques, and disciplines come together under the umbrella of KDD.
patient, provider, payer etc. Therefore data warehouse design presented in this paper can be utilized in any application related to medical claim processing. Data is in normalized form in operational database and it is required to be in de-normalized form in data warehouse, for efficiency and ease of data fetching. Another major difference between operational database and data warehouse is use of primary key. In operational database primary key is logical, IDs are generated with some logic (like Practice code appended with serial number to form a patient ID). While in data warehouse primary key is physical, i.e. an auto generated serial number which increments when new record is inserted, even if the same record is already present. Physical primary key is used in all dimension tables and fact tables of the data
The explosive growth in the amount of data and the challenges for finding interesting patterns from huge amount of data lead to emergence of data mining. Data mining is the process of extracting the interesting (valid, novel, useful and understandable) patterns from the huge data that are actionable and may be used for enterprise’s decision making process. Data mining is one of the core processes of knowledge discovery in databases.
There are many different types of students. All students have their own way of studying and learning material. A student’s attitude is the most determining factor in how well a student performs academically. Some students are eager to learn and try their best; however, some students could care less about learning. Each year students decide whether they will succeed or fail in school. All students fall into one category or another. Students can be classified into three categories: Overachievers, Average Joes, and Do Not Give a Rips.
In the information age, a lot of data is generated from everywhere. Together with the incoming of information technology tools, so all the data are collected and waiting to be converted to information and knowledge. Therefore, the information industry provides useful information to many areas such as market analysis, science, decision-making and customer relationship. Data mining is the integration between analytical techniques and database system. Previously, it has only database query, data processing or transactional processing, which is insufficient for users to understand the whole data at a time. They cannot answer complex questions such as what are the relationships among items in database. The answers of those questions are more valuable for people. The users need is far exceed database management system ability because of a huge amount of data, so hidden patterns and knowledge should be discovered. Unfortunately, a human ability is limited and people cannot understand a very big dataset by themselves. Thus, the powerful tools are invented to help people to analyze large data. If there are no powerful tools then the huge amounts of data is just pieces of garbage because nobody would like to investigate them. In order to discover hidden patterns or useful information from tremendous data there is a process called “Data mining”.
Data Mining (DM), or Knowledge Discovery is extraction of implicit, hidden trends, previously unknown, and useful information from data. DM research adopted many techniques from research areas like artificial intelligence, statistics and machine learning.
Machine learning is a branch of artificial intelligence that aims at solving real life engineering problems. It provides the opportunity to learn without being explicitly programmed and it is based on the concept of learning from data. It is so much ubiquitously used dozen a times a day that we may not even know it. The advantage of machine learning (ML) methods is that it uses mathematical models, heuristic learning, knowledge acquisitions and decision trees for decision making. Thus, it provides controllability, observability and stability. It updates easily by adding a new patient‘s record.
It’s one of the most effective services that are available today. With the help of data mining, one can discover precious information about the customers and their behavior for a specific set of products and evaluate and analyze, store, mine and load data related to them.
Data in data mining means structured, relational data[6]. Text mining works with unstructured data— texts. Text mining is extraction of useful information from text data it is also known as text data mining or knowledge discovery from textual databases.