Hybrid Approach for Prediction of Cardiovascular Disease Using Class Association Rules and MLP

:  In data mining classification techniques are used to predict group membership for data instances. These techniques are capable of processing a wider variety of data and the output can be easily interpreted. The aim of any classification algorithm is the design and conception of a standard model with reference to the given input. The model thus generated may be deployed to classify new examples or enable a better comprehension of available data.  Medical data classification is the process of transforming descriptions of medical diagnoses and procedures used to find hidden information. Two experiments are performed to identify the prediction accuracy of Cardiovascular Disease (CVD).A hybrid approach for classification is proposed in this paper by combining the results of the associate classifier and artificial neural networks (MLP).  The first experiment is performed using associative classifier to identify the key attributes which contribute more towards the decision by taking the 13 independent attributes as input. Subsequently classification using Multi Layer Perceptrons (MLP) also performed to generate the accuracy of prediction using all attributes. In the second experiment, identified key attributes using associative classifier are used as inputs for the feed forward neural networks for predicting the presence or absence of CVD

Sensing the Web for Induction of Association Rules and their Composition through Ensemble Techniques

Abstract Starting from geophysical data collected from heterogeneous sources, such as meteorological stations and information gathered from the web, we seek unknown connections between the sampled values through the extraction of association rules. These rules imply the co-occurrence of two or more symbols in the same representation, and the rule confidence may vary according to the collected data. We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones. The composition enables the creation of rules that are robust and let emerge a larger number of interesting rules

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

Prevention and Reversal of Peripheral Neuropathy/Peripheral Arterial Disease

This monograph presents a five-step treatment protocol to prevent and reverse Peripheral Neuropathy (PN)/Peripheral Arterial Disease (PAD), based on the following systemic medical principle: at the present time, removal of cause is a necessary, but not necessarily sufficient, condition for restorative treatment to be effective. Implementation of the five-step PN/PAD treatment protocol is as follows: Step 1: Obtain a detailed medical and habit/exposure history from the patient. Step 2: Administer written and clinical performance and behavioral tests to assess the severity of the higher-level symptoms and degradation of executive functions Step 3: Administer laboratory tests (blood, urine, imaging, etc) Step 4: Eliminate ongoing PN/PAD contributing factors Step 5: Implement PN/PAD treatments This individually-tailored PN/PAD treatment protocol can be implemented with the data currently available in the biomedical literature. Additionally, while the methodology developed for this study was applied to comprehensive identification of diagnostics, contributing factors, and treatments for PN/PAD, it is general and applicable to any chronic disease/condition that, like PN/PAD, has an associated substantial research literature. Thus, the protocol and methodology developed to prevent or reverse PN/PAD can be used to prevent or reverse any chronic disease (with the possible exceptions of individuals with strong genetic predispositions to the disease in question or who have suffered irreversible damage from the disease)