A K -means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis

Abstract(#br)This paper proposes a new medical diagnosis algorithm that uses a K -means interval type-2 fuzzy neural network (KIT2FNN). This KIT2FNN classifier uses a K -means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the K -means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient..

Cuckoo inspired algorithms for feature selection in heart disease prediction

Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques