Evaluating the Efficiency of Radial Basis Function Classifier with Different Feature Selection for Identifying Dementia

The Magnetic Resonance Imaging (MRI) based classification process for the classification of dementia is presented in this work. The classifier's performance may be enhanced by means of improving the extracted features that are inputted into its classifier. These MRI images are all duly segmented by making use of the wavelet. For choosing a subset that has optimal features, it may become inflexible and all issues relating to the feature selection will be shown as the NonDeterministic Polynomial (NP)-hard. The work further deals with techniques of optimization that are used in the case of feature selection for picking an optimal feature set. The Principal Component Analysis (PCA) will find an application of a large scale in signal processing. The noise estimation and the source separation are all possible. For this, the Radial Basis Function (RBF) and its classifier have been optimized to this structure by making use of the Genetic Algorithm (GA)-Artificial Immune System (AIS) algorithm. Such an optimized classifier of the RBF will classify a feature set that is provided by the GA, the AIS and the GA-AIS algorithm of feature selection. A classifier will be evaluated on the basis of its performance metrics. All classifiers will be evaluated keeping the accuracy, specificity, and sensitivity in making use of an optimized set of features. The results of the experiment have clearly demonstrated the feature selection and its effectiveness to improve the accuracy of the classification of all the images.

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A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine

Interdisciplinary Sciences Computational Life Sciences ◽

10.1007/s12539-016-0183-6 ◽

2016 ◽

Vol 10 (2) ◽

pp. 244-250

Author(s):

Divya Anand ◽

Babita Pandey ◽

Devendra K. Pandey

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Radial Basis Function ◽

Basis Function ◽

Selection Model ◽

Support Vector ◽

Bhattacharyya Coefficient ◽

Radial Basis

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Feature Extraction and Classification of Segmented ECG Signals Based on Radial Basis Function and Random Forest Methodology

International Journal on Cybernetics & Informatics ◽

10.5121/ijci.2021.100205 ◽

2021 ◽

Vol 10 (02) ◽

pp. 35-45

Author(s):

Rexy J ◽

Velmani P ◽

Rajakumar T.C

Keyword(s):

Heart Disease ◽

Random Forest ◽

Radial Basis Function ◽

Basis Function ◽

Performance Metrics ◽

Heart Diseases ◽

Ecg Signals ◽

Specificity And Sensitivity ◽

Radial Basis ◽

The Given

Heart disease is the major cause of death ratio increase in this decade. Nowadays various people of different age sector undergo the high risk of heart problems and miss their precious life all of a sudden. Early detection of heart disease will save many people’s life well in advance. Heart Diseases are predictable and they can be identified in earlier stage. First basic method to identify heart disease is ElectroCardioGram (ECG) which is the basic recording method of electrical activities of a functioning heart. ECG is the cheapest and painless method to detect the basic heart problems. This paper is an attempt to detect and classify heart beat signals which will serve as the basic step to predict basic and serious issues which may affect the functioning of the heart. The raw ECG signals are extracted and preprocessed to remove unwanted noises which will produce effective results. The preprocessed ECG signals are then are utilized to identify the heart beats which comprise of signals such as P,Q,R,S,T and U. After detecting the heart beats, they are segmented to extract the ECG Features. The temporal and spectral features are extracted from the segmented ECG signals for classification purpose. The extracted feature vectors are utilized to classify the signals. Radial Basis function and Random Forest method are commonly used classification methodologies; hence these two methodologies are applied to classify the ECG Signals into five basic classes. Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database and Noise Stress database are used for this implementation and the classes are identified based on the given dataset parameters. Performance metrics such as accuracy, specificity and sensitivity are computed to find out the best classification methodology among the applied two methodologies. This performance analysis provides a clear comparative view of both the existing methodologies and specifies that Radial Basis function well suits for the given segmented ECG signals and the extracted features. Hence this performance evaluation paves way for best classification algorithm selection or extension of the best methodology and it can be further optimized for better classification result. The implementation process has been carried out using Matlab software environment.

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