Prognosis of Disease that may Occur with Growing Age using Confabulation Based Algorithm

The enduring diagnosis of patient’s medical records might be useful to determine the causes that are responsible for a particular disease. So that, one can take early preventive measures to curtail the risk of diseases that may occur with the growing age. Consequently, this can enhance the life expectancy probability. Here, a new algorithm CMARM is proposed for analysis of symptoms in order to find out the disease that may occur frequently and rarely with growing age. It uses map reduce paradigm inspired by cognitive learning. It is concerned with acquisition of problem-solving skills, intelligence and conscious thought and uses prevailing knowledge to generate new rules. It has been evaluated over synthetic data sets collected from the health data repository. Since, CMARM requires one-time file access therefore, it is consistently faster and also consuming less memory space than the FP tree based algorithm

ENHANCED ALGORITHMS FOR MINING OPTIMIZED POSITIVE AND NEGATIVE ASSOCIATION RULE FROM CANCER DATASET

The most important research aspect nowadays is the data. Association rule mining is vital mining used in data which mines many eventual informations and associations from enormous databases. Recently researchers focus many research challenges to association rule mining. The first challenge is the generation of the frequent and infrequent itemsets from a large dataset more accurately. Secondly how effectively the positive and negative association rule can be mined from both the frequent and infrequent itemsets with high confidence, good quality, and high comprehensibility with reduced time. Predominantly in existing algorithms the infrequent itemsets is not taken into account or rejected. In recent times it is said that useful information are hidden in this itemsets in the case of medical field. The third challenge are to generate is optimised positive and negative association rule. Several existing algorithms have been implemented in order to assure these challenges but many such algorithms produces data losses, lack of efficiency and accuracy which also results in redundant rules. The major issue in using this analytic optimizing method are specifying the activist initialization limit was the quality of the association rule relays on. The proposed work has three methods which mine an optimized PAR and NAR. The first method is the Apriori_AMLMS (Accurate multi-level minimum support) this algorithm derives the frequent and the infrequent itemsets very accurately based on the user-defined threshold minimum support value. The next method is the GPNAR (Generating Positive and Negative Association Rule) algorithm to mine the PAR and NAR from frequent itemsets and PAR and NAR from infrequent itemsets. The third method are to obtain an optimized PAR and NAR using the decidedly efficient swarm intelligence algorithm called the Advance ABC (Artificial Bee Colony) algorithm which proves that an efficient optimized Positive and negative rule can be mined. The Advance ABC is a Meta heuristic technique stimulated through the natural food foraging behaviour of the honey bee creature. The experimental analysis shows that the proposed algorithm can mine exceedingly high confidence non redundant positive and negative association rule with less time

Surgical Management of Gastroesophageal Reflux in Children: Risk Stratification and Prediction of Outcomes

Introduction: Since the 1980s fundoplication, an operation developed for adults with hiatus hernia and reflux symptoms, has been performed in children with gastroesophageal reflux disease (GORD). When compared to adult outcomes, paediatric fundoplication has resulted in higher failure and revision rates. In the first chapter we explore differences in paradigm, patient population and outcomes. Firstly, symptoms are poorly defined and are measured by instruments of varying quality. Secondly, neurological impairment (NI), prematurity and congenital anomalies (oesophageal atresia, congenital diaphragmatic hernia) are prevalent in children. / Purpose: To develop methods for stratifying paediatric fundoplication risk and predicting outcomes based on symptom profile, demographic factors, congenital and medical history. / Methods: Study objectives are addressed in three opera: a symptom questionnaire development (TARDIS:REFLUX), a randomised controlled trial (RCT) and a retrospective database study (RDS). TARDIS: REFLUX: In the second chapter, digital research methods are used to design and validate a symptom questionnaire for paediatric GORD. The questionnaire is a market-viable smartphone app hosted on a commercial platform and trialed in a clinical pilot study. / RCT: In the third chapter, the REMOS trial is reported. The trial addresses the subset of children with NI and feeding difficulties. Participants are randomized to gastrostomy with or without fundoplication. Notably, pre- and post-operative reflux is quantified using pH-impedance. / RDS: In the fourth chapter, data mining and machine learning strategies are applied to a retrospective paediatric GORD database. Predictive modelling techniques applied include logistic regression, decision trees, random forests and market basket analysis. / Results and conclusion: This work makes two key contributions. Firstly, an effective methodology for development of digital research tools is presented here. Secondly, a synthesis is made of literature, the randomised controlled trial and retrospective database modelling. The resulting product is an evidence-based algorithm for the surgical management of children with GORD