Significant Feature Selection Method for Health Domain using Computational Intelligence- A Case Study for Heart Disease

In the medical field, the diagnosing of cardiovascular disease is that the most troublesome task. The diagnosis of heart disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a very vital quantity between the researchers and clinical professionals regarding the economical and correct heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal supply of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the best support for predicting disease with correct case of training and testing. The main idea behind this work is to find relevant heart disease feature among the large number of feature using rough computational Intelligence approach. The proposed feature selection approach performance is better than traditional feature selection approaches. The performances of the rough computation approach is tested with different heart disease data sets and validated with real-time data sets

INTEGRATION OF ROUGH SET THEORY AND GENETIC ALGORITHM FOR OPTIMAL FEATURE SUBSET SELECTION ON DIABETIC DIAGNOSIS

Diabetic diagnosis is an important research in health care domain to analyze relevant microorganisms at an earlier stage. Due to large growth in world’s population, feature subset selection model receives a great deal in any domain of research and also a reliable tool for diabetic diagnosis. Several data mining techniques have been developed to evaluate the significant causes of diabetes with least sets of risk factors. These minimum set is selected without considering the potential significance of the risk factors and optimal feature subset selection, hence it failed to diagnosis the pattern of diabetes accurately. In order to improve the feature subset selection, an Integration of Fuzzy Rough Set Theory and Optimized Genetic algorithm (IFRST-OGA) is introduced. The main objective of the IFRST-OGA is to find optimal risk factors for efficient pattern recognition on diabetes healthcare data. Initially, feature selection is performed using Fuzzy Rough Set Theory (FRST) for diagnosing the diabetes. After that, the Optimized Genetic Algorithm (OGA) is applied which mainly searches for an optimal feature subset through the selection, crossover, and mutation operations to diagnose the disease at an earlier stage. This helps to identify the risk factor and diagnosing the diabetes disease efficiently. Experimental results show that the proposed IFRST-OGA increases the performance in terms of true positive rate, computation time and diabetes diagnosing accuracy

Finding patterns in student and medical office data using rough sets

Data have been obtained from King Khaled General Hospital in Saudi Arabia. In this project, I am trying to discover patterns in these data by using implemented algorithms in an experimental tool, called Rough Set Graphic User Interface (RSGUI). Several algorithms are available in RSGUI, each of which is based in Rough Set theory. My objective is to find short meaningful predictive rules. First, we need to find a minimum set of attributes that fully characterize the data. Some of the rules generated from this minimum set will be obvious, and therefore uninteresting. Others will be surprising, and therefore interesting. Usual measures of strength of a rule, such as length of the rule, certainty and coverage were considered. In addition, a measure of interestingness of the rules has been developed based on questionnaires administered to human subjects. There were bugs in the RSGUI java codes and one algorithm in particular, Inductive Learning Algorithm (ILA) missed some cases that were subsequently resolved in ILA2 but not updated in RSGUI. I solved the ILA issue on RSGUI. So now ILA on RSGUI is running well and gives good results for all cases encountered in the hospital administration and student records data.Master's These

Soft Covering Based Rough Sets and Their Application

Soft rough sets which are a hybrid model combining rough sets with soft sets are defined by using soft rough approximation operators. Soft rough sets can be seen as a generalized rough set model based on soft sets. The present paper aims to combine the covering soft set with rough set, which gives rise to the new kind of soft rough sets. Based on the covering soft sets, we establish soft covering approximation space and soft covering rough approximation operators and present their basic properties. We show that a new type of the soft covering upper approximation operator is smaller than soft upper approximation operator. Also we present an example in medicine which aims to find the patients with high prostate cancer risk. Our data are 78 patients from Selçuk University Meram Medicine Faculty