Analysis Diabetes Mellitus on Complications with Data Mining

Analysis Diabetes Mellitus on Complications with information Mining M. Mayilvaganan T.SivaranjaniAbstractDiabetes mellitus is incredible growing and seems to be emerging as a main public health problem for our surface area.The prevalence of diabetes is rapidly increasing all over the world.Data mining provides more no of tools and techniques that tin after part be applied to this processed information to gather up hidden patterns. It is used to provide healthc ar professionals an additional source of knowledge for making decisions. This research is compend about diabetes prevalence, complications, and continueing from complications.Keywords diabetes mellitus, info analysis, data mining, diabetes prevalence, complicationsINTRODUCTIONDiabetes is a group of metabolic diseasescaused by the lack of insulin in the organic structure or inability to produce as normal. In contemporary world most of folk are distressed by diabetes, which affects a large population across the world .The prevalence of diabetes for all age-groups ecumenic was estimated to be 2.8% in 2000 and 4.4% in 2030. The rack up number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. The prevalence of diabetes is higher in men than women, but there are more women with diabetes than men. The urban population in developing countries is projected to double between 2000 and 20309.TYPES OF DIABETESType 1 diabetesThis character of diabetes usually develops during childhood or teens and is characterized by a severe deficiency of insulin secretion resulting from atrophy of the islets of Langerhans and causing hyperglycemia and a marked tendency toward diabetic acidosiscalled in additioninsulin-dependent diabetes, insulin-dependent diabetes mellitus, juvenile diabetes, juvenile-onset diabetes, fictitious character 1 diabetes mellitus 6.Type 2 diabetesIts mostly distressed in adulthood and is exacerbated by obesity and an inactive lifestyle. This dise ase lots has no symptoms, is usually diagnosed by tests that indicate glucose intolerance, and is treated with changes in diet and an exercise uninterruptedly 7.Gestational diabetesGestational diabetes mellitus (GDM) is defined as any dot of glucose intolerance with onset or first recognition during pregnancy 8. The definition applies whether insulin or only diet modification is used for treatment and whether or not the condition persists after pregnancy. It does not exclude the possibility that unrecognized glucose intolerance may drop antedated or begun concomitantly with the pregnancy.Fig 1Prevalence estimates of diabetes mellitus (DM), 2010 South-East Asian RegionTo estimating the prevalence of diabetes for the years 2000, 2010 and 2030, data on case numbers and national prevalence of impaired glucose tolerance are presented in chart 10.The total populations of the regions and the populations aged from 20-79 years are shown in Figure 2. From the figure we clearly known West ern Pacific Region, which includes China, and the South-East Asian Region, which has India as a member, have the greatest numbers of people 10.Fig2 Top 5- Number of people with diabetes (20-79 age group), 2000, 2010 and 2030Fig 3 Top 5-Prevalence of impaired glucose tolerance (20-79 age group), 2010 and 2030COMPLICATIONS OF DIABETESSkin ComplicationsTo be more consciousness for symptoms of skin infections and other skin disorders common in people with diabetes.Eye ComplicationsYearly or six months once keep regular check up avoid assay of glaucoma, cataracts and other eye problems. Due to nation survey in India eye complication was rare.NeuropathyNerve damage from diabetes is called diabetic neuropathy .The majority of people with diabetes have any one of type of nerve damage.Foot ComplicationsThe largest parts of diabetes patients have foot damages. Take care of our foot as much as like face. Before bed we have to clean and dry our foot. Through the regular excise and walking we c an avoid this complication.Kidney unhealthiness (Nephropathy) elevated BP and glucose is major cause this. Keep your diabetes and blood pressure under control to lesser the chance of getting kidney disease.High Blood PressureHigh blood pressure is also called hypertension. It raises more complications like heart attack, stroke, eye problems, and kidney disease.StrokeKeep up blood glucose, blood pressure, and cholesterol in approximate level. It to be reduces your risk of stroke. Most of the patients affected stroke by hypertension.DATA MINING TECHNIQUESIn healthcare industry nowadays generates huge amounts of complex data about patients, hospitals resources, disease diagnosis, electronic patient records, and medical devices etc.These data are a key resource to be stored, processed and analyzed for knowledge line that enables to support for cost-savings and decision making. Data mining is the process of exploration and analysis, by automatic or semiautomatic means, of large quanti ties of data in order to discover meaningful patterns and rules 11.Data mining could be on the whole useful in medicine when there is no dispositive evidence favoring an exacting treatment option. establish on patients profile, history, fleshly inspection, diagnosis and utilizing previous treatment patterns, new treatment policy can be successfully recommended. Data mining is finding raise structure (patterns, statistical models, relationships) in databases. 12.Logistic regression models are used to compare hospital profiles and based on that risk-factors are analyses in data mining. unsubstantial neural networks are used in medical diagnosis. It produces a clinically relevant output based on sample database, and constructs the probability of a real pathology or classification of biomedical objects. Due to the generous plasticity of input data, ANNs have verified useful in the analysis of blood and urine samples of diabetic patients 13. Unsupervised learning engrosses identifyin g clusters and associations. Clusters are faction the analogous subtypes and make group. Using regression analysis, associate the following attributes as age, family history, increasing socio-economic status and decreasing physical activity and find high frequency of cause which type of diabetes distressed.No one can tell literally, which algorithm is best for any problem, because data sets from discordant data sources. To applying algorithm in training set and came to the solution, which is suite .data set be consists of missing values, noise, and outliers. Cleaning data from noise and outliers and handling missing values, and therefore finding the exact subset of data and prepares them for successful data mining. Missing values are filled up with the most familiar value and combinations of particular attribute-value pairs are significant within a dataset.DATA SET REPRESENTATIONCollecting patients medical details based on that calculated BMI, body type, required calories, actual calories, complications, risk factors. The table 1 specified for risk analysis and table 2 for diagnosed complications. Some of the attributes of datasets are BMI, require weight, BMI index, working industry, consume habit, blood group, life style, and require calorie based on sex, family history,PCOS,HBA1c,Smoker, drinker, type of DM,dignosed age, symptoms, no of years, Gestational diabetes history, baby weight, order of baby, control DM,Fast food,BP,food intervals, intake limits.Table 1. Characteristics of risk analysis data set234227212214313120Monitor the following attributes as high HBA1c, stationary, job, BP, Life style, disease caused after diabetes diagnosed, undiet, smoking, drinking habits regularly can avoid more complications.Table 2. Characteristics of complications data set1361112111672442022105ConclusionIndia is top most country in prevalence of diabetes. Number of people with diabetes in our country in 2010 50.8 million and will be estimated 87.0 in 203010. Diabetes complication fatality rates also raised and prevent these government or social organizations, health cares must provide education or training focuses on self-care behaviors, such(prenominal) as healthy eating, being active, and monitoring blood sugar.Many of the steps necessitate to take to avert one of those complications may really sustain to prevent them all. This kind of education or training is a mutual process in which diabetes educators help people with or at risk for diabetes gain the knowledge. Data mining bring a set of tools, techniques and method that can be functional to this processed data to determine hidden patterns. Data mining algorithms are used to extract informative patterns from raw data. Physicians can identify effective treatments and best observation, and also patients receive improved and more affordable healthcare services.It is help to manage and monitor patients can have important utility in diabetes mellitus and analysis complicates. In the future, we plan to demonstrate the usefulness of this kind of study by measuring the extent to which data mining approaches empower clinical research and practice.References1. Dandona, Lalit, et al. Population based assessment of diabetic retinopathy in an urban population in southern India.British journal of ophthalmology83.8 (1999) 937-940.2. Sanders, Reginald J., and M. Roy Wilson. Diabetes-related eye disorders.Journal of the National Medical Association85.2 (1993) 104.3. Gckler, D., et al. Diabetes and kidneys.Deutsche medizinische Wochenschrift (1946)138.18 (2013) 949-955.4. Berger, A. and Berger, C.R. Data mining as a tool for research and knowledge development in nursing.CINMay/June 2004.5. Stephens, S. and Tamayo, P. Supervised and unsupervised data mining techniques for life sciences.Curr Drug DiscJune 2003.6. Ewing, D. J., I. W. Campbell, and B. F. Clarke. The natural history of diabetic autonomic neuropathy.QJM49.1 (1980) 95-108.7.http//www.merriam-webster.com/dictionary/type%201% 20diabetes8. Metzger BE, Coustan DR (Eds.) Proceedings of the Fourth internationalistic Work-shop-Conference on Gestational Diabetes Mellitus.Diabetes Care21 (Suppl. 2)B1B167,19989. Wild, Sarah, et al. Global prevalence of diabetes estimates for the year 2000 and projections for 2030.Diabetes care27.5 (2004) 1047-1053.10. Sicree, Richard, et al. The global burden.Diabetes and impaired glucose tolerance. Baker IDI Heart and Diabetes Institute(2010).11. Berry, Michael JA, and Gordon Linoff. Data Mining Techniques . J. (2004).12. Bradley, Paul S., Usama M. Fayyad, and Olvi L. Mangasarian. Mathematical programming for data mining formulations and challenges.INFORMS Journal on Computing11.3 (1999) 217-238.13. Amato, Filippo, et al. Artificial neural networks in medical diagnosis.Journal of Applied Biomedicine11.2 (2013) 47-58.13. Data Mining Technologies for Blood Glucose and Diabetes Management 603 , Riccardo Bellazzi, Ph.D.,and Ameen Abu-Hanna, Ph.D.14.http//health.india.com/diseases- conditions/sweet-nothings-discard-myths-to-successfully-manage-diabetes/15. Application of data mining Diabetes health care in young and old patients Abdullah A. Aljumah, Mohammed Gulam Ahamad, Mohammad Khubeb Siddiqui16. An Analysis of Diabetes Risk Factors Using Data Mining Approach Akkarapol Sangasoongsong and Jongsawas Chongwatpol Oklahoma State University, Stillwater, OK 74078, the States17. The need for obtaining accurate nationwide estimates of diabetes prevalence in India Rationale for a national study on diabetes R.M. Anjana, M.K. Ali*, R. Pradeepa, M. Deepa, M. Datta, R. Unnikrishnan, M. Rema V. Mohan18. Am I at risk for type 2 diabetes? Taking Steps to Lower Your Risk of Getting Diabetes19. http//www.diabetes.org/living-with-diabetes/complications/