Review on Heart Disease Prediction System using Data Mining Techniques

Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of heart disease are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is nearly 100% in one paper [10] and in [6]. So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease

An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database

Background: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. Objectives: The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). Patients and Methods: A population of 6647 individuals without diabetes, aged � 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures. Results: In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) � 30 kg/m2, family history of diabetes, wrist circumference > 16.5 cm and waist to height � 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio � 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence. Conclusions: Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone. © 2015, Research Institute For Endocrine Sciences and Iran Endocrine Society

Utilization and Costs of Compounded Medications for Commercially Insured Patients, 2012 – 2013

Background: Although compounding has a long-standing tradition in clinical practice, insurers and pharmacy benefit managers have instituted policies to decrease claims for compounded medications, citing questions about their safety, efficacy, high costs, and lack of Food and Drug Administration (FDA) approval. There are no reliable published data on the extent of compounding by community pharmacists nor the fraction of patients who use compounded medications. Prior research suggests that compounded medications represent a relatively small proportion of prescription medications, but these surveys were limited by small sample sizes, subjective data collection methods, and low response rates. Objective: To determine the number of claims for compounded medications, on a per user per year (PUPY) basis, and the average ingredient cost of these claims among commercially insured patients in the United States (US) for 2012 and 2013. Methods: This study used prescription claims data from a nationally representative sample of commercially insured members whose pharmacy benefits were managed by a large pharmacy benefit management company. A retrospective claims analysis was conducted from January 1, 2012 through December 31, 2013. Annualized prevalence, cost, and utilization estimates were drawn from the data. All prescription claims were adjusted to 30-day equivalents. Data mining techniques (association rule mining) were employed in order to identify the most commonly combined ingredients in compounded medications. Results: The prevalence of compound users was 1.1% (245,285) of eligible members in 2012 and 1.4% (323,501) in 2013, an increase of 27.3%. Approximately 66% of compound users were female and the average age of a compound user was approximately 42 years throughout the study period. The geographic distribution of compound user prevalence was consistent across the US. Compound users’ prescription claims increased 36.6%, from approximately 7.1 million to approximately 9.7 million prescriptions from 2012 to 2013. The number of claims for compounded medications increased by 34.2% from 486,886 to 653,360 during the same period. PUPY utilization remained unchanged at 2 prescriptions per year from 2012 to 2013. The most commonly compounded drugs were similar for all adult age groups, and represented therapies typically indicated for chronic pain or hormone replacement therapy. The average ingredient cost for compounded medications increased by 130.3% from $308.49 to$710.36 from 2012 to 2013. The average ingredient cost for these users’ non-compounded prescriptions increased only 7.7%, from $148.75 to$160.20. For comparison, the average ingredient cost for all prescription users’ claims was $81.50 in 2012, and increased by 3.8$84.57 in 2013. Conclusions: Compound users represented 1.4% of eligible members in 2013. The average ingredient cost for compound users’ compounded prescriptions ($710.36) was greater than for non-compounded prescriptions ($160.20). The one-year increase in average compounded prescription costs (130.3%) was also greater than for non-compounded prescriptions (7.7%). Although prevalence of compound users and the PUPY utilization for compounded prescriptions increased only slightly between 2012 and 2013, the mean and median cost of compounded medications increased dramatically during this time. Text mining revealed that drug combinations characteristic of topical pain formulations were among the most frequently compounded medications for adults