scholarly journals Arteriovenous Fistula Flow Dysfunction Surveillance: Early Detection Using Pulse Radar Sensor and Machine Learning Classification

Biosensors ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 297
Author(s):  
Cheng-Hsu Chen ◽  
Teh-Ho Tao ◽  
Yi-Hua Chou ◽  
Ya-Wen Chuang ◽  
Tai-Been Chen
Keyword(s):  
Arteriovenous Fistula ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
High Morbidity ◽  
Radar Sensor ◽  
Svm Classification ◽  
Ultrasound Dilution ◽  
Machine Learning Classification ◽  
Pulse Radar

Vascular Access (VA) is often referred to as the “Achilles heel” for a Hemodialysis (HD)-dependent patient. Both the patent and sufficient VA provide adequacy for performing dialysis and reducing dialysis-related complications, while on the contrary, insufficient VA is the main reason for recurrent hospitalizations, high morbidity, and high mortality in HD patients. A non-invasive Vascular Wall Motion (VWM) monitoring system, made up of a pulse radar sensor and Support Vector Machine (SVM) classification algorithm, has been developed to detect access flow dysfunction in Arteriovenous Fistula (AVF). The harmonic ratios derived from the Fast Fourier Transform (FFT) spectrum-based signal processing technique were employed as the input features for the SVM classifier. The result of a pilot clinical trial showed that a more accurate prediction of AVF flow dysfunction could be achieved by the VWM monitor as compared with the Ultrasound Dilution (UD) flow monitor. Receiver Operating Characteristic (ROC) curve analysis showed that the SVM classification algorithm achieved a detection specificity of 100% at detection thresholds in the range from 500 to 750 mL/min and a maximum sensitivity of 95.2% at a detection threshold of 750 mL/min.

scholarly journals Obscure Image Classification and Restoration using Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 8231-8236
Keyword(s):  
Kernel Estimation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classification ◽  
Vector Machines ◽  
Blurred Image ◽  
Simulation Results ◽  
Blurred Images ◽  
Identification And Characterization ◽  
Restoration Model

A restoration and classification computation for blurred image which depends on obscure identification and characterization is proposed in this paper. Initially, new obscure location calculation is proposed to recognize the Gaussian, Motion and Defocus based blurred locales in the image. The degradation-restoration model referred with pre-processing followed by binarization and features extraction/classification algorithm applied on obscure images. At this point, support vector machine (SVM) classification algorithm is proposed to cluster the blurred images. Once the obscure class of the locales is affirmed, the structure of the obscure kernels of the blurred images are affirmed. At that point, the obscure kernel estimation techniques are embraced to appraise the obscure kernels. At last, the blurred locales are re-established utilizing nonblind image deblurring calculation and supplant the blurred images with the restored images. The simulation results demonstrate that the proposed calculation performs well

Based on SVM Classification of Connection Pools Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2435-2438
Author(s):  
Juan Zhao ◽  
Hui Yun Xiong
Keyword(s):  
Classification Algorithm ◽  
Experimental Results ◽  
Svm Classifier ◽  
Database Access ◽  
Service Platform ◽  
Buffer Pool ◽  
Svm Classification ◽  
Connection Pool

The connection pool technology has become a deal with large amount of data requested a solution that is widely used now. This paper used the SVM classification algorithm for classified all database requests quickly, so the corresponding database request could be assigned to different connection pool distribution. We applied the connection pool to measurement service platform and tested on the accuracy of the SVM classifier and buffer pool hit ratios of the connection pool module. The experimental results show that the connection pools can improve the efficiency of database access obviously.

scholarly journals Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes

2020 ◽  
pp. 1-13
Author(s):  
Fillemon S. Enkono ◽  
Nalina Suresh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mobile Applications ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Classification

Fraudulent e-wallet deposit notification SMSes designed to steal money and goods from m-banking users have become pervasive in Namibia. Motivated by an observed lack of mobile applications to protect users from such deceptions, this study evaluated the ability of machine learning to detect the fraudulent e-wallet deposit notification SMSes. The naïve Bayes (NB) and support vector machine (SVM) classifiers were trained to classify both ham (desired) SMSes and scam (fraudulent) e-wallet deposit notification SMSes. The performances of the two classifier models were then evaluated. The results revealed that the SVM classifier model could detect the fraudulent SMSes more efficiently than the NB classifier.

219A machine learning classification algorithm to detect patients with paroxysmal atrial fibrillation during sinus rhythm

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Tachmatzidis ◽  
D Filos ◽  
I Chouvarda ◽  
D Mouselimis ◽  
A Tsarouchas ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
P Wave ◽  
Support Vector ◽  
Svm Classifier ◽  
P Waves ◽  
Machine Learning Classification ◽  
Increased Risk ◽  
Wave Morphology ◽  
The Mean

Abstract Background Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders. Purpose The aim of this study is to identify P-wave morphology patterns encountered in patients with Paroxysmal AF (PAF) and to develop a classifier discriminating PAF patients from healthy volunteers. Methods Three-dimensional 1000Hz ECG signals of 5 minutes duration were obtained through the use of a Galix GBI-3S Holter monitor from a total of 68 PAF patients and 52 healthy individuals. Signal pre-processing, consisting of denoising, QRS auto-detection, and ectopic beats removal was performed and a signal window of 250ms prior to the Q-wave (Pseg) was considered for every single beat. P‑wave morphology analysis based on the dynamic application of the k‑means clustering process was performed. For those Pseg that were assigned in the largest cluster, the mean P-wave was computed. The correlation of every P-wave with the mean P-wave of the main cluster was calculated. In case that it exceeded a prespecified threshold, the P-wave was allocated to the main morphology. For the remaining P‑waves, the same approach was followed once again, and the secondary morphology was extracted (picture). The P-waves of the dominant morphology were further analyzed using wavelet transform, whereas time-domain characteristics were also extracted. A Support Vector Machine (SVM) model was created using the Gaussian Radial Basis Function kernel and the forward feature selection wrapper approach was followed. ECGs were allocated to the training, internal validation, and testing datasets in a 3:1:1 ratio. Results The percentage of P-waves following the main morphology in all three leads was lower in PAF patients (91.2 ±7.3%) than in healthy subjects (96.1 ±3.5%, p = 0.02). Classification between the two groups highlighted 7 features, while the SVM classifier resulted in a balanced accuracy of 91.4 ± 0.2% (sensitivity 94.2 ± 0.3%, specificity 88.6 ± 0.1%) Conclusion An Artificial Intelligence based ECG Classifier can efficiently identify PAF patients during normal sinus rhythm. Abstract Figure.

scholarly journals Prediction of Heart Disease using Machine Learning

2020 ◽  
pp. 150-166
Author(s):  
Ankit Singh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Mean Squared Error ◽  
Classification Algorithm ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Machine Learning Classification ◽  
Squared Error ◽  
Clinical Data Analysis ◽  
Learning Technique

Cardiovascular Disease is the leading cause of death (Approximately, 17 million people every year) in the all the area of the world. Prediction of heart disease is the critical challenge in the area of the clinical data analysis. The objective of paper is to build the model for predicting the Heart Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of heart disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model. The result of the study indicates that KNN algorithm is effective in predicting the model with the accuracy of the 85.71% and having a very low mean squared error.

Chronic Kidney Disease Prediction Using Different Algorithms

2020 ◽  
pp. 06-13
Author(s):  
Harsh Vardhan Singh
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Mean Squared Error ◽  
Classification Algorithm ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Squared Error ◽  
Health Damage ◽  
Specific Symptoms

Chronic Kidney Disease (CKD) is a disease which doesn't shows symptoms at all or in some cases it doesn't show any disease specific symptoms it is hard to predict, detect and prevent such a disease and this could be lead to permanently health damage, but machine learning can be hope in this problem it is best in prediction and analysis. The objective of paper is to build the model for predicting the Chronic Kidney Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of Chronic Kidney Disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model.

scholarly journals Prediksi Gejala Autism Spectrum Disorders pada Remaja Menggunakan Optimasi Particle Swarm Optimization dan Algoritma Support Vector Machine

2020 ◽  
Vol 4 (2) ◽  
pp. 113
Author(s):  
Ilham Kurniawan
Keyword(s):  
Autism Spectrum Disorder ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Particle Swarm ◽  
Autism Spectrum ◽  
Classification Algorithm ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Svm Classification ◽  
Proposed Model

Abstrak: Telah ada peningkatan prevalensi diagnosis Autism Spectrum Disorder (ASD) secara global selama dekade terakhir. Perkiraan prevalensi ASD yang diperbarui dan keseluruhan di Asia akan membantu para profesional kesehatan untuk mengembangkan strategi kesehatan masyarakat yang relevan. Dalam penelitian ini, mengusulkan metode untuk prediksi gejala ASD menggunakan teknik integrasi seleksi fitur PSO dan algoritma Support Vector Machine. Penelitian ini menggunakan dataset dari UCI repository. Model yang diusulkan meliputi penerapan seleksi fitur menggunakan  particle swarm optimization (PSO), dengan algoritma pengklasifikasi. Hasil akhir akan dilakukan perbandingan pengujian dan analisa terhadap model prediksi yang memiliki tingkat akurasi tertinggi atau terbaik dalam prediksi gejala ASD. Dalam penelitian ini menggunakan dataset UCI repository yaitu data ASD pada remaja, data tersebut memiliki jumlah data sebanyak 104 instance dan 21 atribut, 41 orang tidak menderita ASD dan 63 orang menderita ASD, tools yang digunakan untuk menerapkan model usulan menggunakan aplikasi Weka versi 3.8.4. Untuk mengetahui model usulan yang diajukan pada penelitian ini, pertama menguji dengan klasifikasi tunggal SVM, dan kedua, menguji dengan seleksi fitur PSO dan algoritma klasifikasi SVM. Untuk mengetahui apakah seleksi fitur PSO berpengaruh terhadap performa algoritma klasifikasi SVM. Pengujian pertama, nilai akurasi yang dihasilkan oleh algoritma klasifikasi SVM adalah sebesar 89.42%, dan nilai AUC sebesar 0.891. Berdasarkan pengujian yang kedua yaitu menggunakan seleksi fitur PSO, seleksi fitur PSO dapat meningkatkan performa algoritma klasifikasi SVM sebesar 2,88% dan nilai AUC sebesar 0,024.   Kata kunci: Autism Spectrum Disorder, Particle Swarm Optimizatio, Support Vector Machine   Abstract: There has been an increase in the prevalence of diagnoses of Autism Spectrum Disorder (ASD) globally over the past decade. Updated and overall ASD prevalence estimates in Asia will help health professionals to develop relevant public health strategies. In this study, proposing a method for ASD symptom prediction using PSO feature selection integration techniques and the Support Vector Machine algorithm. This study uses a dataset from the UCI repository. The proposed model includes the application of feature selection using particle swarm optimization (PSO), with the classification algorithm. The final result will be a comparison test and analysis of prediction models that have the highest or best accuracy in predicting ASD symptoms. In this study using the UCI repository dataset, ASD data on adolescents, the data has 104 data and 21 attributes, 41 people do not suffer from ASD and 63 people suffer from ASD, tools used to implement the proposed model using the Weka application version 3.8.4 . To find out the proposed model proposed in this study, firstly testing with SVM single classification, and secondly, testing with PSO feature selection and SVM classification algorithm. To find out whether the PSO feature selection affects the performance of the SVM classification algorithm. The first test, the accuracy value generated by the SVM classification algorithm is 89.42%, and the AUC value is 0.891. Based on the second test using PSO feature selection, PSO feature selection can improve the performance of the SVM classification algorithm by 2.88% and the AUC value of 0.024.   Keywords: Autism Spectrum Disorder, Particle Swarm Optimizatio, Support Vector Machine.

scholarly journals Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

2021 ◽  
Author(s):  
M Arasumani ◽  
Aditya Singh ◽  
Milind Bunyan ◽  
V.V. Robin
Keyword(s):  
Remote Sensing ◽  
Native Species ◽  
Ground Truth ◽  
Google Earth ◽  
Support Vector ◽  
Svm Classification ◽  
Machine Learning Classification ◽  
Tropical Grasslands ◽  
Exotic Tree ◽  
Sentinel 2

AbstractInvasive alien species (IAS) threaten tropical grasslands and native biodiversity and impact ecosystem service delivery, ecosystem function, and associated human livelihoods. Tropical grasslands have been dramatically and disproportionately lost to invasion by trees. The invasion continues to move rapidly into the remaining fragmented grasslands impacting various native grassland-dependent species and water streamflow in tropical montane habitats. The Shola Sky Islands of the Western Ghats host a mosaic of native grasslands and forests; of which the grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus and Pines) since the 1950s. The invasion intensities, however, differ between these species wherein Acacia mearnsii and Pinus patula are highly invasive in contrast to Eucalyptus globulus. These disparities necessitate distinguishing these species for effective grassland restoration. Further, these invasive alien trees are highly intermixed with native species, thus requiring high discrimination abilities to native species apart from the non-native species.Here we assess the accuracy of various satellite and airborne remote sensing sensors and machine learning classification algorithms to identify the spatial extent of native habitats and invasive trees. Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape. Sensor combinations thus include hyperspectral, multispectral and radar data and present tradeoffs in associated costs and ease of procurement. Classification methods tested include Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM recover the highest classification skill (Overall −98%, Kappa-0.98); while CART and RF yielded < 90% accuracy. Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy with RF and SVM classification; but only with higher coverage of ground control points. AVIRIS-NG imagery was able to accurately (97%) demarcate the Acacia invasion front while Sentinel-1 and Sentinel-2 data failed. Our results suggest that Sentinel-2 images could be useful for detecting the native and non-native forests with more ground truth points, but hyperspectral data (AVIRIS-NG) permits distinguishing, native and non-native tree species and recent invasions with high precision using limited ground truth points. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration, or to assess the success of restoration activities, and several data procurement and analysis steps may have to be simplified.

scholarly journals A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3854 ◽  
Author(s):  
Paul Chao ◽  
Pei-Yu Chiang ◽  
Yung-Hua Kao ◽  
Tse-Yi Tu ◽  
Chih-Yu Yang ◽  
...  
Keyword(s):  
Arteriovenous Fistula ◽  
Dynamic Range ◽  
Digital Signal ◽  
Kernel Functions ◽  
Support Vector ◽  
Dimensionless Number ◽  
Svm Classifier ◽  
Type Ii Error ◽  
Vector Machines ◽  
Signal Processing Algorithms

A portable, wireless photoplethysomography (PPG) sensor for assessing arteriovenous fistula (AVF) by using class-weighted support vector machines (SVM) was presented in this study. Nowadays, in hospital, AVF are assessed by ultrasound Doppler machines, which are bulky, expensive, complicated-to-operate, and time-consuming. In this study, new PPG sensors were proposed and developed successfully to provide portable and inexpensive solutions for AVF assessments. To develop the sensor, at first, by combining the dimensionless number analysis and the optical Beer Lambert’s law, five input features were derived for the SVM classifier. In the next step, to increase the signal-noise ratio (SNR) of PPG signals, the front-end readout circuitries were designed to fully use the dynamic range of analog-digital converter (ADC) by controlling the circuitries gain and the light intensity of light emitted diode (LED). Digital signal processing algorithms were proposed next to check and fix signal anomalies. Finally, the class-weighted SVM classifiers employed five different kernel functions to assess AVF quality. The assessment results were provided to doctors for diagonosis and detemining ensuing proper treatments. The experimental results showed that the proposed PPG sensors successfully achieved an accuracy of 89.11% in assessing health of AVF and with a type II error of only 9.59%.

scholarly journals A new model for early diagnosis of alzheimer's disease based on BAT-SVM classifier

2021 ◽  
Vol 10 (2) ◽  
pp. 759-766
Author(s):  
Shereen A. Taie ◽  
Wafaa Ghonaim
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Svm Classifier ◽  
New Model ◽  
Machine Learning Classification ◽  
Biomedical Image ◽  
Proposed Model ◽  
The Brain

Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due to its ability to measure regional changes in the brain that reflect disease progression to detect early stages of the disease. In this paper, we propose a new model that adopts Bat for parameter optimization problem of Support vector machine (SVM) to diagnose Alzheimer’s disease via MRI biomedical image. The proposed model uses MRI for biomedical image classification to diagnose three classes; normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The proposed model based on segmentation for the most involved areas in the disease hippocampus, the features of MRI brain images are extracted to build feature vector of the brain, then extracting the most significant features in neuroimaging to reduce the high dimensional space of MRI images to lower dimensional subspace, and submitted to machine learning classification technique. Moreover, the model is applied on different datasets to validate the efficiency which show that the new Bat-SVM model can yield promising acceptable level of accuracy reached to 95.36 % using maximum number of bats equal to 50 and number of generation equal to 10.

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