scholarly journals Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3422 ◽  
Author(s):  
Chiang ◽  
Chao ◽  
Tu ◽  
Kao ◽  
Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Support Vector ◽  
Flow Volume ◽  
Machine Learning Classification ◽  
Blood Flow Volume ◽  
Sensor Device

The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog–digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer–Lambert’s law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF.

scholarly journals P07.04: Uterine artery blood flow volume is reduced in human pregnancies with increase utero-placental downstream impedance

2006 ◽  
Vol 28 (4) ◽  
pp. 569-569
Author(s):  
S. Boito ◽  
S. Rigano ◽  
G. Pennati ◽  
L. Mandia ◽  
A. Padoan ◽  
...  
Keyword(s):  
Blood Flow ◽  
Uterine Artery ◽  
Flow Volume ◽  
Artery Blood Flow ◽  
Blood Flow Volume

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021 ◽  
Vol 10 (2) ◽  
pp. 736-745
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Support Vector ◽  
Two Stage ◽  
Machine Learning Classification ◽  
Second Stage ◽  
Testing Data ◽  
Stage Classification ◽  
Positive Dataset

The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

Cerebral Blood Flow Volume in Posterior Circulation Ischemic Disorders

2021 ◽  
pp. 154431672110539
Author(s):  
Anastasiya Yu. Vishnyakova ◽  
Nataliya M. Medvedeva ◽  
Alexander B. Berdalin ◽  
Svetlana E. Lelyuk ◽  
Vladimir G. Lelyuk
Keyword(s):  
Blood Flow ◽  
Healthy Volunteers ◽  
Internal Carotid ◽  
Duplex Sonography ◽  
Posterior Circulation ◽  
Control Group ◽  
Vertebrobasilar Insufficiency ◽  
Flow Volume ◽  
Blood Flow Volume ◽  
Vertebral Arteries

Objective: The aim of this study was to determine blood flow volume (BFV) in the normal state and its features in patients with acute posterior circulation ischemic strokes (PCIS) and vertebrobasilar insufficiency (VBI) using color duplex sonography (DS).Methods: The study included DS data from 96 patients with verified PCIS (66 men and 30 women, aged 64±13 years) and 29 adults with VBI (17 men and 12 women, aged 66±11 years). The control group consisted of 65 healthy male volunteers of different ages.Results: In asymptomatic healthy volunteers, there was a significant decrease in BFV in the internal carotid artery (ICA) with age (502 ml/min in young people, 465 ml/min in the older subgroup) with rS = −0.24 ( p = 0.05), and the aggregated BFV in the vertebral arteries (VAs) turned out to be almost constant (141–143 ml/min). In patients with VBI, the aggregated BFV in the VAs (144 ml/min) did not differ from that in healthy volunteers, but the BFV values in the ICAs were significantly lower (325 ml/min). In patients with PCIS, the aggregated BFV in the ICAs was also significantly lower (399 ml/min) than in the control group but did not significantly differ from that in patients with VBI. In patients with PCIS, there was a significant decrease in the aggregated BFV in the VAs (105 ml/min), which distinguished this group from other examined patients.Conclusions: A significant decrease the BFV in the VA was observed only in patients with PCIS and was associated with the presence of steno-occlusive diseases (SOD) more often in the left VA. Patients with VBI had the most pronounced decrease in BFV in the ICA.

Blood flow, volume, and transit time in the pulmonary microvasculature using laser-Doppler

1994 ◽  
Vol 76 (6) ◽  
pp. 2643-2650 ◽  
Author(s):  
T. S. Hakim ◽  
E. Gilbert ◽  
E. M. Camporesi
Keyword(s):  
Blood Flow ◽  
Blood Volume ◽  
Transit Time ◽  
Flow Rate ◽  
Venous Pressure ◽  
Laser Doppler ◽  
Flow Volume ◽  
Doppler Flowmetry ◽  
Blood Flow Volume ◽  
Wide Range

Capillary transit time is determined by the ratio of capillary volume to flow rate. Exercise-induced hypoxemia is thought to occur because of the short transit time of erythrocytes in capillaries. The effect of flow rate on capillary volume (recruitment vs. distension) is controversial. In a perfused left lower lobe preparation in canine lungs, we used laser-Doppler flowmetry (model ALF21R) to monitor changes in blood flow, volume, and transit time in the microvasculature near the subpleural surface. Changes in total flow, blood volume, and total transit time (tt) were also measured. The results showed that microvascular volume approached maximum when flow rate was at resting value (0.4 l/min) and pressure in the pulmonary artery was > 6 mmHg relative to the level of the capillaries. In contrast, the total blood volume increased gradually over a wide range of flow rates. When flow increased 4.2 times (from 155 to 650 ml/min), tt decreased from 7.32 to 3.53 s; meanwhile, microvascular flow increased from 6.0 to 12.7 units and microvascular transit time decreased from 3.14 to 1.81 units. The changes in microvascular volume and transit time were essentially independent of whether the venous pressure was higher or lower than alveolar pressure. At very high flow (6–10 times resting value), tt fell gradually to approximately 1 s. Direct monitoring of transit time with the laser-Doppler also revealed a gradual decline in microvascular transit time as flow rate increased from 2 to 10 times the normal flow. (ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals Applying Machine Learning for Improving Performance Classification on Driving Behavior

2021 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo
Keyword(s):  
Machine Learning ◽  
Traffic Accident ◽  
Large Scale ◽  
Detection System ◽  
Difficult Problem ◽  
Sensor Data ◽  
Driving Safety ◽  
Support Vector ◽  
Classification Methods ◽  
Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

scholarly journals Classifications of Breast Cancer Diagnosis using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Breast Cancer Diagnosis ◽  
Performance Comparison ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Machine Learning Classification

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

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