scholarly journals Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data

2021 ◽  
Vol 18 (14) ◽  
pp. 7635
Author(s):  
Wen-Yu Ou Yang ◽  
Cheng-Chien Lai ◽  
Meng-Ting Tsou ◽  
Lee-Ching Hwang
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Prediction Models ◽  
Assessment Tool ◽  
Learning Algorithms ◽  
Community Dwelling ◽  
Machine Learning Algorithms ◽  
Screening Tools ◽  
Support Vector ◽  
Health Examination

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models, respectively. The ANN, SVM, RF, and LoR models in men, and the ANN, SVM, and RF models in women performed significantly better than the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We have demonstrated that machine learning algorithms improve the performance of screening for osteoporosis. By incorporating the models in clinical practice, patients could potentially benefit from earlier diagnosis and treatment of osteoporosis.

A Novel Approach of Weighted Support Vector Machine with Applied Chance Theory for Forecasting Air Pollution Phenomenon in Egypt

2018 ◽  
Vol 17 (01) ◽  
pp. 1850001 ◽  
Author(s):  
Nabil Mohamed Eldakhly ◽  
Magdy Aboul-Ela ◽  
Areeg Abdalla
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Management Control ◽  
Air Pollutant ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Chance Theory

The particulate matter air pollutant of diameter less than 10 micrometers (PM[Formula: see text]), a category of pollutants including solid and liquid particles, can be a health hazard for several reasons: it can harm lung tissues and throat, aggravate asthma and increase respiratory illness. Accurate prediction models of PM[Formula: see text] concentrations are essential for proper management, control, and making public warning strategies. Therefore, machine learning techniques have the capability to develop methods or tools that can be used to discover unseen patterns from given data to solve a particular task or problem. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The main objective is to study the modification of a single machine learning algorithm — support vector machine (SVM) — applying the chance weight of the target variable, based on the chance theory, to the corresponding dataset point to be superior to the ensemble machine learning algorithms. The results of this study are outperforming of the SVM algorithms when modifying and combining with the right theory/technique, especially the chance theory over other modern ensemble learning algorithms.

Prediction of Dansgaard-Oeschger events using machine learning

2021 ◽  
Author(s):  
Nuno Moniz ◽  
Susana Barbosa
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Ice Core ◽  
Model Performance ◽  
Predictive Performance ◽  
Oxygen Isotopic Composition ◽  
Machine Learning Algorithms ◽  
Support Vector

<p>The Dansgaard-Oeschger (DO) events are one of the most striking examples of abrupt climate change in the Earth's history, representing temperature oscillations of about 8 to 16 degrees Celsius within a few decades. DO events have been studied extensively in paleoclimatic records, particularly in ice core proxies. Examples include the Greenland NGRIP record of oxygen isotopic composition.<br>This work addresses the anticipation of DO events using machine learning algorithms. We consider the NGRIP time series from 20 to 60 kyr b2k with the GICC05 timescale and 20-year temporal resolution. Forecasting horizons range from 0 (nowcasting) to 400 years. We adopt three different machine learning algorithms (random forests, support vector machines, and logistic regression) in training windows of 5 kyr. We perform validation on subsequent test windows of 5 kyr, based on timestamps of previous DO events' classification in Greenland by Rasmussen et al. (2014). We perform experiments with both sliding and growing windows.<br>Results show that predictions on sliding windows are better overall, indicating that modelling is affected by non-stationary characteristics of the time series. The three algorithms' predictive performance is similar, with a slightly better performance of random forest models for shorter forecast horizons. The prediction models' predictive capability decreases as the forecasting horizon grows more extensive but remains reasonable up to 120 years. Model performance deprecation is mostly related to imprecision in accurately determining the start and end time of events and identifying some periods as DO events when such is not valid.</p>

scholarly journals Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1406
Author(s):  
Fadratul Hafinaz Hassan ◽  
Mohd Adib Omar
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Recurrent Stroke ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Recurrent Strokes ◽  
Public Datasets

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%.

Machine learning algorithms to predict seizure due to acute tramadol poisoning

2021 ◽  
pp. 096032712199191
Author(s):  
B Behnoush ◽  
E Bazmi ◽  
SH Nazari ◽  
S Khodakarim ◽  
MA Looha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Clinical Care ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Predictive Values ◽  
Arterial Blood

Introduction: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making. Methods: Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013–2019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) algorithms. Area Under the Curve (AUC) and other diagnostic criteria were used to assess performance of models. Results: In 909 patients, 544 (59.8%) experienced seizures. The important predictors of seizure were sex, pulse rate, arterial blood oxygen pressure, blood bicarbonate level and pH. SVM (AUC = 0.68), NB (AUC = 0.71) and ANN (AUC = 0.70) models outperformed k-NN model (AUC = 0.58). NB model had a higher sensitivity and negative predictive value and k-NN model had higher specificity and positive predictive values than other models. Conclusion: A perfect prediction model may help improve clinicians’ decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.

scholarly journals Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome

2021 ◽  
Vol 8 ◽  
Author(s):  
Siyi Yuan ◽  
Yunbo Sun ◽  
Xiongjian Xiao ◽  
Yun Long ◽  
Huaiwu He
Keyword(s):  
Machine Learning ◽  
Systemic Inflammatory Response Syndrome ◽  
Inflammatory Response ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Systemic Inflammatory Response ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Icu Patients ◽  
New Onset

Background: Distinguishing ICU patients with candidaemia can help with the precise prescription of antifungal drugs to create personalized guidelines. Previous prediction models of candidaemia have primarily used traditional logistic models and had some limitations. In this study, we developed a machine learning algorithm trained to predict candidaemia in patients with new-onset systemic inflammatory response syndrome (SIRS).Methods: This retrospective, observational study used clinical information collected between January 2013 and December 2017 from three hospitals. The ICU patient data were used to train 4 machine learning algorithms–XGBoost, Support Vector Machine (SVM), Random Forest (RF), ExtraTrees (ET)–and a logistic regression (LR) model to predict patients with candidaemia.Results: Of the 8,002 cases of new-onset SIRS (in 7,932 patients) included in the analysis, 137 new-onset SIRS cases (in 137 patients) were blood culture positive for candidaemia. Risk factors, such as fungal colonization, diabetes, acute kidney injury, the total number of parenteral nutrition days and renal replacement therapy, were important predictors of candidaemia. The XGBoost machine learning model outperformed the other models in distinguishing patients with candidaemia [XGBoost vs. SVM vs. RF vs. ET vs. LR; area under the curve (AUC): 0.92 vs. 0.86 vs. 0.91 vs. 0.90 vs. 0.52, respectively]. The XGBoost model had a sensitivity of 84%, specificity of 89% and negative predictive value of 99.6% at the best cut-off value.Conclusions: Machine learning algorithms can potentially predict candidaemia in the ICU and have better efficiency than previous models. These prediction models can be used to guide antifungal treatment for ICU patients when SIRS occurs.

scholarly journals Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

2021 ◽  
Vol 18 (5) ◽  
pp. 2584
Author(s):  
Cheng-Chien Lai ◽  
Wei-Hsin Huang ◽  
Betty Chia-Chen Chang ◽  
Lee-Ching Hwang
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Success Rate ◽  
Prediction Models ◽  
Smoking Status ◽  
Medical Center ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

scholarly journals Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4324
Author(s):  
Moaed A. Abd ◽  
Rudy Paul ◽  
Aparna Aravelli ◽  
Ou Bai ◽  
Leonel Lagos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sliding Contact ◽  
Tactile Sensation ◽  
Support Vector ◽  
Textured Surfaces ◽  
K Nearest Neighbor ◽  
Tactile Sensors ◽  
Time Frequency

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Long-term streamflow forecasting for the Cascade Reservoir System of Han River using SWAT with CFS output

2018 ◽  
Vol 50 (2) ◽  
pp. 655-671
Author(s):  
Tian Liu ◽  
Yuanfang Chen ◽  
Binquan Li ◽  
Yiming Hu ◽  
Hui Qiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Distribution Models ◽  
Han River ◽  
Reservoir System

Abstract Due to the large uncertainties of long-term precipitation prediction and reservoir operation, it is difficult to forecast long-term streamflow for large basins with cascade reservoirs. In this paper, a framework coupling the original Climate Forecasting System (CFS) precipitation with the Soil and Water Assessment Tool (SWAT) was proposed to forecast the nine-month streamflow for the Cascade Reservoir System of Han River (CRSHR) including Shiquan, Ankang and Danjiangkou reservoirs. First, CFS precipitation was tested against the observation and post-processed through two machine learning algorithms, random forest and support vector regression. Results showed the correlation coefficients between the monthly areal CFS precipitation (post-processed) and observation were 0.91–0.96, confirming that CFS precipitation post-processing using machine learning was not affected by the extended forecast period. Additionally, two precipitation spatio-temporal distribution models, original CFS and similar historical observation, were adopted to disaggregate the processed monthly areal CFS precipitation to daily subbasin-scale precipitation. Based on the reservoir restoring flow, the regional SWAT was calibrated for CRSHR. The Nash–Sutcliffe efficiencies for three reservoirs flow simulation were 0.86, 0.88 and 0.84, respectively, meeting the accuracy requirement. The experimental forecast showed that for three reservoirs, long-term streamflow forecast with similar historical observed distribution was more accurate than that with original CFS.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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