scholarly journals Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument

2020 ◽  
Vol 9 (2) ◽  
pp. 72-79
Author(s):  
Sari Ayu Wulandari ◽  
Sutikno Madnasri ◽  
Ratih Pramitasari ◽  
Susilo Susilo
Keyword(s):  
Diabetes Mellitus ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Early Diabetes ◽  
Feature Extraction Method ◽  
Fuzzy C Means

The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%.

SEGMENT CLASSIFICATION OF ECG DATA AND CONSTRUCTION OF SCATTER PLOTS USING PRINCIPAL COMPONENT ANALYSIS

2008 ◽  
Vol 08 (03) ◽  
pp. 421-458 ◽  
Author(s):  
M. P. S. CHAWLA
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Feature Selection ◽  
Order Statistics ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signal ◽  
Characteristic Points ◽  
Scatter Plots ◽  
Ecg Data

In many medical applications, feature selection is obvious; but in medical domains, selecting features and creating a feature vector may require more effort. The wavelet transform (WT) technique is used to identify the characteristic points of an electrocardiogram (ECG) signal with fairly good accuracy, even in the presence of severe high-frequency and low-frequency noise. Principal component analysis (PCA) is a suitable technique for ECG data analysis, feature extraction, and image processing — an important technique that is not based upon a probability model. The aim of the paper is to derive better diagnostic parameters for reducing the size of ECG data while preserving morphology, which can be done by PCA. In this analysis, PCA is used for decorrelation of ECG signals, noise, and artifacts from various raw ECG data sets. The aim of this paper is twofold: first, to describe an elegant algorithm that uses WT alone to identify the characteristic points of an ECG signal; and second, to use a composite WT-based PCA method for redundant data reduction and better feature extraction. PCA scatter plots can be observed as a good basis for feature selection to account for cardiac abnormalities. The study is analyzed with higher-order statistics, in contrast to the conventional methods that use only geometric characteristics of feature waves and lower-order statistics. A new algorithm — viz. PCA variance estimator — is developed for this analysis, and the results are also obtained for different combinations of leads to find correlations for feature classification and useful diagnostic information. PCA scatter plots of various chest and augmented ECG leads are obtained to examine the varying orientations of the ECG data in different quadrants, indicating the cardiac events and abnormalities. The efficacy of the PCA algorithm is tested on different leads of 12-channel ECG data; file no. 01 of the Common Standards for Electrocardiography (CSE) database is used for this study. Better feature extraction is obtained for some specific combinations of leads, and significant improvement in signal quality is achieved by identifying the noise and artifact components. The quadrant analysis discussed in this paper highlights the filtering requirements for further ECG processing after performing PCA, as a primary step for decorrelation and dimensionality reduction. The values of the parameters obtained from the results of PCA are also compared with those of wavelet methods.

scholarly journals Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs

Computation ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 78
Author(s):  
Shengkun Xie
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Power Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Eeg Signals ◽  
Feature Extraction Method ◽  
Functional Principal Component ◽  
Low Dimensional

Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems.

Hot Rolling State Analysis Based on Kernel Principal Component Analysis and Information Entropy

2012 ◽  
Vol 572 ◽  
pp. 7-12
Author(s):  
Fei He ◽  
Quan Yang ◽  
Bao Jian Wang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Information Entropy ◽  
Hot Rolling ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Process Data ◽  
Feature Extraction Method ◽  
Information Metric

With more and more process data acquired from manufacturing process, extracting useful information to build empirical models of past successful operations is urgently required to get higher product quality. Clustering is the important data mining methods, where feature extraction is a significant factor to ensure the accurate rate of clustering and classification. As a common non-linear feature extraction method, kernel principal component analysis (KPCA) uses the variance as the information metric, but the variance is not always effective in some cases. Since information entropy is nonlinear and can effectively represent the dependencies of features, the Renyi entropy is used as the information metric to extract the feature in this paper. Simulation data, Tennessee Eastman and hot rolling process data are used for model validation. As a result the proposed method has better performance on feature extraction, compared with traditional KPCA.

scholarly journals Feature Extraction of Chest X-ray Images and Analysis using PCA and kPCA

2018 ◽  
Vol 8 (5) ◽  
pp. 3392 ◽  
Author(s):  
Roopa H ◽  
Asha T
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Computer System ◽  
Feature Selection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Data Mining Algorithm ◽  
X Ray ◽  
Chest X Ray ◽  
Disease Analysis

<p class="abstract">Tuberculosis (TB) is an infectious disease caused by mycobacterium which can be diagnosed by its various symptoms like fever, cough, etc. Tuberculosis can also be analyzed by understanding the chest x-ray of the patient which is revealed by an expert physician .The chest x-ray image contains many features which cannot be directly used by any computer system for analyzing the disease. Features of chest x-ray images must be understood and extracted, so that it can be processed to a form to be fed to any computer system for disease analysis. This paper presents feature extraction of chest x-ray image which can be used as an input for any data mining algorithm for TB disease analysis. So texture and shape based features are extracted from x-ray image using image processing concepts. The features extracted are analyzed using principal component analysis (PCA) and kernel principal component analysis (kPCA) techniques. Filter and wrapper feature selection method using linear regression model were applied on these techniques. The performance of PCA and kPCA are analyzed and found that the accuracy of PCA using wrapper approach is 96.07%   when compared to the accuracy of kPCA which is 62.50%. PCA performs well than kPCA with a good accuracy.</p>

scholarly journals Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Debabrata Samanta ◽  
M. P. Karthikeyan ◽  
Marimuthu Karuppiah ◽  
Dalima Parwani ◽  
Manish Maheshwari ◽  
...  
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Negative Impact ◽  
Feature Selection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Classification Model ◽  
Newborn Jaundice

One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.

scholarly journals Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak ◽  
Sridhar Krishnan ◽  
Pietro Lio
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Multiscale Methods ◽  
Kernel Principal Component Analysis ◽  
Simulation Data ◽  
Translation Invariant ◽  
Feature Extraction Method

We introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters required in the calculation of a kernel matrix. This new methodology incorporates multiscale methods into KPCA for transforming multiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in KPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered simulation data. We show that WKPCA is an effective feature extraction method for transforming a variety of multidimensional clustered data into data with a higher level of linearity among the data attributes. That brings an improvement in the accuracy of simple linear classifiers. Based on the analysis of the simulation data sets, we observe that multiscale translation invariant wavelet kernels for KPCA has an enhanced performance in feature extraction. The application of the proposed method to real data is also addressed.

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