Developing A Diabetes Detection System

Diabetes is a very common chronic medial disorder and expected to be a big medical challenge of the twenty first century (Clark, 2004). It is a condition, in which the glucose level in the blood becomes so high that the body is unable to utilize it properly. This long-term condition results when the pancreas of the victim is either not able to produce enough insulin (sometimes even cannot produce any insulin) or the insulin produced cannot work properly. Diabetes is divided into two main types: Type 1 and Type 2. Type 1, also known as insulin dependent diabetes milletus (Masharani, 2008) cannot be prevent while Type 2 can be prevented but if the patient is at pre-diabetes stage.

There were five CDC expectations that Ms. Jones used as a guideline for the Diabetes Recognition Program. The expectations included the following throughout the program, diabetes knowledge, session attendance during the core phase, weight loss achieved during the core phase and lastly documentation of body weights during the post-core phase. According to the results found in category 1-5 of the appendix ZZ, Ms. Jones class did meet the CDC expectations. Category 1 which was the pre and posttest including a control group of ten students averaged 44% for the pretest and posttest average 71%. The control group exceeded the CDC expectations because the average was to be at least 50%, which in the results was achieved 20% more. Category 2 which

Risk factors for developing type 2 diabetes include behavioral factors like being physically inactive and overweight, but also include genetic predispositions. Simply being of American Indian background is considered a risk factor in and of itself.1 This capstone project will attempt to highlight certain aspects of highly successful diabetes prevention programs and how they may relate to American Indian populations, and speak to how well the programs with high success rates will fit into American Indian populations. This capstone will provide a reliable set of guidelines for the design of new diabetes prevention curricula by health care administration officials, health care practitioners, and public health officers. Because the risk of type 2 diabetes is much higher in these populations1, this capstone has great biological significance in that it may aid in more effective education about diabetes and the reduction in the incidence of diabetes in these populations. In addition to broad guidelines, this project will attempt to produce an algorithm or flow chart to aid clinicians in designing their own diabetes prevention curricula based on both individual patient characteristics and population characteristics. This project will not have a strict experimental design in that it is aimed more at synthesizing the entirety of

Diabetes mellitus, otherwise known as simply diabetes, is a disorder of the metabolism where the body cannot produce or use the hormone “insulin.” The most common symptoms for having this disorder are extreme thirst and the production of excessive amounts of urine. However, the symptom that doctors use as a definite is glucose concentration. A diabetic person's glucose concentration is usually usually higher than normal. To monitor the blood glucose concentration, these people use glucose meters.

Diabetes is a metabolic disorder in which energy cannot be obtained from the body through normal mechanisms of action. Insulin is required to assist in moving glucose across cell membranes. There are two types of diabetes. Type one, which is characterized by the pancreas no longer having the ability to make insulin, and Type two, which is characterized by the resistance of the tissue receptors of the body to insulin. Diabetes is sometimes called “The Silent Killer,” as it has significant rates of illness and mortality. There is a large correlation between diabetes and cardiovascular disease, end-stage renal disease, amputations, and blindness (Gulanick, Myers 2014).

Finally, in the last component, the four main applications of big data analytics in healthcare are present. These are queries, reports, On-Line Analytical Processing (OLAP), and data mining. With the help of various techniques and technologies from fields like statistics, applied mathematics, computer science, economics, big data is aggregated, transformed, analyzed and finally visualized in healthcare.

Type 1 diabetes is a common autoimmune disorder in which the beta islet cells of the pancreas produce little to no insulin. This makes it very difficult for the body to maintain safe blood glucose levels. The condition is chronic and incurable, but most patients can successfully keep it under control with glucose monitoring devices and insulin injections or an insulin pump. With these options, patients must frequently manually test their blood sugar and give themselves insulin. This may be difficult for young patients, especially when they may need to get up and test themselves overnight.

Doctors and researchers are findings new cures and solutions save the rising issues of diabetes and bring better health care facilities and technology in the field to help sustain longer, healthier life. As Hovorka, Roman, et al suggest, The technologies include the monitoring devices and the blood test devices that can be used by the patients themselves helping them to keep track of their blood sugar level. The solution does not know the blood sugar level but to act on it accordingly (2006). The use of technology and the development of online records are japing the patients to share their records with the doctors and experts overseas to help them adjusting to new drugs and technological tools.

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

Hello! In this section, I’m going to be talking about the step-by-step procedure in which food is ordinarily digested, and how diabetes might affect this process. It might seem like a boring topic, but I’ll try to add some fun facts somewhere in here just to make it more interesting.

Diabetes is the general term referring to various groups of metabolic diseases that are characterised by Polydipsia and Polyuria. When used alone, the term refers to Diabetes Mellitus. Diabetes Millitus specifically, is identified by hyperglycaemia, resulting from defects in insulin action, insulin secretion, or both ("Diagnosis and Classification of Diabetes Mellitus," n.d.).

The rise of Big Data and its attendant complexities has spawned a whole ecosystem to support the ever growing requirements of a 24x7 world. One of the key technologies coming out of the initial stages of Big Data has been Hadoop. Conceived in response to the rapidly growing needs of Yahoo!’s search engine, Hadoop provides a mechanism to store and collect vast amounts of data across a highly distributed environment using commodity hardware.

Diabetes is a rapidly growing problem, currently affecting 300 million people worldwide . and its incidence is expected to increase rapidly in the coming decades[0]. Diabetes can lead to complications such as serious as lower-limb amputations, blindness, and cardiovascular disease [1].Continuous and accurate monitoring of patient blood glucose levels is critical for diagnosis and management of the disease.

Notwithstanding the common understanding that data mining applies to data analysis problems with vast amounts of data and is solved by resorting to effective combination of analytical methods, the term data mining is still unspecific, largely due to its process-based nature. For this reason a large variety of methods are considered part of data mining, including computer science approaches, such as multidimensional databases, machine learning, soft computing and data visualization, and statistical-based methods, including hypothesis testing, clustering, classification, and regression. Data mining technologies have proven to be useful and effective in different areas, including marketing, customer relationship management, engineering, and biomedical research [20, 21]. This research uses the applications of data mining technique on the collected data, particularly the analysis of diabetes mellitus (DM) data. Health is wealth a saying in Tamil proverb. Jazan lies on the south west corner of Saudi Arabia. The region has a lot of potential to become a hot spot for beach and marine tourism, as well as wellness resorts (SPA) surrounded by nature and mountainous terrain. Due to modernization, urbanization and the improvement in technology there is a drastic change in life style. The change in life style leads to many health problems in humans. Prevalence of Diabetes Mellitus is keeps on

The Hadoop is a framework of free code in Java to run applications that manipulate large amounts of data in distributed environments. The Hadoop is composed by the file system HDFS (Hadoop Distributed File System) and a parallel execution environment. Within this environment, or better, the Hadoop framework, several subprojects can be found, such as the implementation of MapReduce, the distributed data management system called HBase, data flow language r and structure for parallel execution called Pig.