A Random Forest based predictor for medical data classification using feature ranking

Display Omitted A CAD system for disease diagnosis using Differential Evolution with Global Information (DEGI) based Back Propagation (BP) is proposed.The CAD system is trained and tested using six benchmark dataset.DEGI overcomes the premature convergence due to stagnation.The CAD system can be used by junior clinicians for medical decision support. A Computer-Aided Diagnostic (CAD) system that uses Artificial Neural Network (ANN) trained by drawing in the relative advantages of Differential Evolution (DE), Particle Swarm Optimization (PSO) and gradient descent based backpropagation (BP) for classifying clinical datasets is proposed. The DE algorithm with a modified best mutation operation is used to enhance the search exploration of PSO. The ANN is trained using PSO and the global best value obtained is used as a seed by the BP. Local search is performed using BP, in which the weights of the Neural Network (NN) are adjusted to obtain an optimal set of NN weights. Three benchmark clinical datasets namely, Pima Indian Diabetes, Wisconsin Breast Cancer and Cleveland Heart Disease, obtained from the University of California Irvine (UCI) machine learning repository have been used. The performance of the trained neural network classifier proposed in this work is compared with the existing gradient descent backpropagation, differential evolution with backpropagation and particle swarm optimization with gradient descent backpropagation algorithms. The experimental results show that DEGI-BP provides 85.71% accuracy for diabetes, 98.52% for breast cancer and 86.66% for heart disease datasets. This CAD system can be used by junior clinicians as an aid for medical decision support.

Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL) The system would help the doctors to automate heart disease diagnosis and to enhance the medical care In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system After that, the rules for the fuzzy system are created from the sample data Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed At last, the experimentation is performed by means of publicly available UCI datasets, ie, Cleveland, Hungarian and Switzerland datasets The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%

An Efficient System for Heart Disease Prediction using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

Novel design of unsupervised ANNs for solving nanofluidic problems in mechanics.Hybrid computing GA-IPA is exploited for finding design parameters of networks.Design scheme is tested effectively on variant fluid flow and heat transfer scenarios.Correctness of scheme is verified by closely matched results from standard solutions.Statistical performance indices validate consistent accuracy and convergence. In the present study, a new soft computing framework is developed for solving nanofluidic problems based on fluid flow and heat transfer of multi-walled carbon nanotube (MWCNT) along a flat plate with Navier slip boundary with the help of artificial neural networks (ANNs), Genetic Algorithms (GAs), Interior-Point Algorithm (IPA), and hybridized approach GA-IPA. Original PDEs associated with the problem are transformed into system of nonlinear ODEs using similarity transformation. Mathematical model of transformed system is constructed by exploiting the strength of universal function approximation ability of ANNs and an unsupervised error function is formulated for the system in a least mean square sense. Learning of the design variable of the networks is carried out with GAs supported with IPA for rapid local convergence. The design scheme is applied to solve number of variants by taking water, engine oil, and kerosene oil as a base fluids mixed with different concentrations of MWCNTs. The reliability and effectiveness of the design scheme is measured with the help of results of statistical analysis based on sufficient large number of independent runs of the algorithms rather than single successful run. The comparative studies of the proposed solution are made with standard numerical results in order to establish the correctness of the given scheme.

S. Muthukrishnan

Papers

AGFS: Adaptive Genetic Fuzzy System for medical data classification