scholarly journals Cardiovascular Risk Detection Through Big Data Analysis

2020 ◽  
Vol 5 (2) ◽  
pp. 1-11
Author(s):  
Miguel A. Sánchez-Acevedo ◽  
Zaydi Anaí Acosta-Chi ◽  
Ma. del Rocío Morales-Salgado
Keyword(s):  
Nearest Neighbor ◽  
State Level ◽  
Mortality Data ◽  
K Nearest Neighbor ◽  
Web Tool ◽  
Health And Nutrition ◽  
Risk Detection ◽  
Unhealthy Diet ◽  
The Cost

Cardiovascular diseases are the main cause of mortality in the world. As more people suffer from diabetes and hypertension, the risk of cardiovascular disease (CVD) increases. A sedentary lifestyle, an unhealthy diet, and stressful activities are behaviors that can be changed to prevent CVD. Taking measures to prevent CVD lowers the cost of treatments and reduces mortality. Data-driven plans generate more effective results and can be applied to groups with similar characteristics. Currently, there are several databases that can be used to extract information in real time and improve decision making. This article proposes a methodology for the detection of CVD and a web tool to analyze the data more effectively. The methodology for extracting, describing, and visualizing data from a state-level case study of CVD in Mexico is presented. The data is obtained from the databases of the National Institute of Statistics and Geography (INEGI) and the National Survey of Health and Nutrition (ENSANUT). A k-nearest neighbor (KNN) algorithm is proposed to predict missing data.

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

scholarly journals An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Farrukh Khan ◽  
Taher M. Ghazal ◽  
Raed A. Said ◽  
Areej Fatima ◽  
Sagheer Abbas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elderly People ◽  
Nearest Neighbor ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
The Cost

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result.

scholarly journals COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHM ON HOTEL REVIEW SENTIMENT ANALYSIS (CASE STUDY: LUMINOR HOTEL PECENONGAN)

2020 ◽  
Vol 16 (1) ◽  
pp. 59-64
Author(s):  
Jaja Miharja ◽  
Jordy Lasmana Putra ◽  
Nur Hadianto
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Business Decisions ◽  
Machine Learning Classification ◽  
Business People ◽  
Auc Value ◽  
The Right

Analysis of hotel review sentiment is very helpful to be used as a benchmark or reference for making hotel business decisions today. However, all the review information obtained must be processed first by using an algorithm. The purpose of this study is to compare the Classification Algorithm of Machine Learning to obtain information that has a better level of accuracy in the analysis of hotel reviews. The algorithm that will be used is k-NN (k-Nearest Neighbor) and NB (Naive Bayes). After doing the calculation, the following accuracy level is obtained: k-NN of 60,50% with an AUC value of 0.632 and NB of 85,25% with an AUC value of 0.658. These results can be determined by the right algorithm to assist in making accurate decisions by business people in the analysis of hotel reviews using the NB Algorithm.

scholarly journals Disparities in COVID-19 Related Mortality in U.S. Prisons and the General Population

2020 ◽  
Author(s):  
Kathryn M Nowotny ◽  
David Cloud ◽  
Alysse G. Wurcel ◽  
Lauren Brinkley-Rubinstein
Keyword(s):  
General Population ◽  
Single Case ◽  
Prison Population ◽  
State Level ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Mortality Ratio ◽  
Prison Systems ◽  
Related Mortality

We provide an analysis of COVID-19 mortality data to assess the potential magnitude of COVID-19 among prison residents. Data were pooled from Covid Prison Project and multiple publicly available national and state level sources. Data analyses consisted of standard epidemiologic and demographic estimates. A single case study was included to generate a more in-depth and multi-faceted understanding of COVID-19 mortality in prisons. The increase in crude COVID-19 mortality rates for the prison population has outpaced the rates for the general population. People in prison experienced a significantly higher mortality burden compared to the general population (standardized mortality ratio (SMR) = 2.75; 95% confidence interval = 2.54, 2.96). For a handful of states (n = 5), these disparities were more extreme, with SMRs ranging from 5.55 to 10.56. Four states reported COVID-19 related death counts that are more than 50% of expected deaths from all-causes in a calendar year. The case study suggested there was also variation in mortality among units within prison systems, with geriatric facilities potentially at highest risk. Understanding the dynamic trends in COVID-19 mortality in prisons as they move in and out of hotspot status is critical.

Feature Selection for Helicopter Swashplate Bearing Fault Diagnosis

2017 ◽  
Author(s):  
Yong Wang ◽  
Lin Li
Keyword(s):  
Nearest Neighbor ◽  
Ball Bearing ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Bearing Fault Diagnosis ◽  
Feature Extraction And Selection ◽  
Frequency Domains ◽  
Selection For ◽  
Selection Of

This paper provides a case study of diagnosing helicopter swashplate ball bearing faults using vibration signals. We develop and apply feature extraction and selection techniques in the time, frequency, and joint time-frequency domains to differentiate six types of swashplate bearing conditions: low-time, to-be-overhauled, corroded, cage-popping, spalled, and case-overlapping. With proper selection of the features, it is shown that even the simple k-nearest neighbor (k-NN) algorithm is able to correctly identify these six types of conditions on the tested data. The developed method is useful for helicopter swashplate condition monitoring and maintenance scheduling. It is also helpful for testing the manufactured swashplate ball bearings for quality control purposes.

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