scholarly journals Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Xiaodong Ding ◽  
Feng Cheng ◽  
Robert Morris ◽  
Cong Chen ◽  
Yiqin Wang
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Radial Artery ◽  
Pulse Wave ◽  
Disease Diagnosis ◽  
Recursive Feature Elimination ◽  
Physiological Parameter ◽  
Support Vector ◽  
External Interference ◽  
Pulse Waves

BACKGROUND The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals. OBJECTIVE The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data. METHODS Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier. RESULTS It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted. CONCLUSIONS We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.

scholarly journals Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation

10.2196/18134 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e18134
Author(s):  
Xiaodong Ding ◽  
Feng Cheng ◽  
Robert Morris ◽  
Cong Chen ◽  
Yiqin Wang
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Radial Artery ◽  
Pulse Wave ◽  
Disease Diagnosis ◽  
Recursive Feature Elimination ◽  
Physiological Parameter ◽  
Support Vector ◽  
External Interference ◽  
Pulse Waves

Background The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals. Objective The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data. Methods Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier. Results It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted. Conclusions We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.

scholarly journals Predicting failures of Molteno and Baerveldt glaucoma drainage devices using machine learning models

2019 ◽  
Author(s):  
Paul Morrison ◽  
Maxwell Dixon ◽  
Arsham Sheybani ◽  
Bahareh Rahmani
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Intraocular Pressure ◽  
Glaucoma Drainage Device ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Learning Models ◽  
Glaucoma Drainage Devices ◽  
Pressure Lowering ◽  
Machine Learning Models

AbstractThe purpose of this retrospective study is to measure machine learning models’ ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patient’s race, age, sex, preoperative intraocular pressure, preoperative visual acuity, number of intraocular pressure-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final intraocular pressure greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. Overall, the best classifier was logistic regression.

scholarly journals Predicting Failures of Molteno and Baerveldt Glaucoma Drainage Devices Using Machine Learning Models

2020 ◽  
Author(s):  
Paul Morrison ◽  
Maxwell Dixon ◽  
Arsham Sheybani ◽  
Bahareh Rahmani
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Glaucoma Drainage Device ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Learning Models ◽  
Demographic Information ◽  
Drainage Device ◽  
Glaucoma Drainage Devices ◽  
Machine Learning Models

The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device (GDD) failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patient's race, age, sex, preoperative intraocular pressure (IOP), preoperative visual acuity, number of IOP-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final IOP greater than 18 mm Hg, reduction in IOP less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. Overall, the best classifier was logistic regression.

scholarly journals Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Kristiawan Kristiawan ◽  
Andreas Widjaja
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Pearson Correlation ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Learning Technology ◽  
K Nearest Neighbor ◽  
Store Location

Abstract  — The application of machine learning technology in various industrial fields is currently developing rapidly, including in the retail industry. This study aims to find the most accurate algorithmic model so that it can be used to help retailers choose a store location more precisely. By using several methods such as Pearson Correlation, Chi-Square Features, Recursive Feature Elimination and Tree-based to select features (predictive variables). These features are then used to train and build models using 6 different classification algorithms such as Logistic Regression, K Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Neural Network to classify whether a location is recommended or not as a new store location. Keywords— Application of Machine Learning, Pearson Correlation, Random Forest, Neural Network, Logistic Regression.

scholarly journals Apply Machine Learning Methods to Predict Failure of Glaucoma Drainage

2021 ◽  
Vol 11 (1) ◽  
pp. 1-12
Author(s):  
Paul Morrison ◽  
Maxwell Dixon ◽  
Arsham Sheybani ◽  
Bahareh Rahmani
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Intraocular Pressure ◽  
Random Forest ◽  
Glaucoma Drainage Device ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Demographic Information ◽  
Drainage Device ◽  
Machine Learning Methods

The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of 165 patients were used. Potential predictors included the patients' race, age, sex, preoperative intraocular pressure (IOP), preoperative visual acuity, number of IOP-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final IOP greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. With a small amount of data, the best classfier was logistic regression, but with more data, the best classifier was the random forest.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

scholarly journals Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

2021 ◽  
Vol 11 (12) ◽  
pp. 5727
Author(s):  
Sifat Muin ◽  
Khalid M. Mosalam
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degree Of Freedom ◽  
Absolute Velocity ◽  
Support Vector ◽  
Damage State ◽  
Structural Health ◽  
Cumulative Absolute Velocity

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

Detecting Face Touching Using Smartwatches to Mitigate the Spread of COVID-19: Pilot Study (Preprint)

2021 ◽  
Author(s):  
Chen Bai ◽  
Yu-Peng Chen ◽  
Adam Wolach ◽  
Lisa Anthony ◽  
Mamoun Mardini
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Respiratory Diseases ◽  
Window Size ◽  
Support Vector ◽  
Accelerometer Data ◽  
Respiratory Illnesses ◽  
Motion Data ◽  
Machine Learning Methods

BACKGROUND Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. Real-time biofeedback of face touching can potentially mitigate the spread of respiratory diseases. The gap addressed in this study is the lack of an on-demand platform that utilizes motion data from smartwatches to accurately detect face touching. OBJECTIVE The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identifying motion signatures that are mapped accurately to face touching. METHODS Participants (n=10, 50% women, aged 20-83) performed 10 physical activities classified into: face touching (FT) and non-face touching (NFT) categories, in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Then, data features were extracted from consecutive non-overlapping windows varying from 2-16 seconds. We examined the performance of state-of-the-art machine learning methods on face touching movements recognition (FT vs NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees and random forest. RESULTS Machine learning models were accurate in recognizing face touching categories; logistic regression achieved the best performance across all metrics (Accuracy: 0.93 +/- 0.08, Recall: 0.89 +/- 0.16, Precision: 0.93 +/- 0.08, F1-score: 0.90 +/- 0.11, AUC: 0.95 +/- 0.07) at the window size of 5 seconds. IAR models resulted in lower performance; the random forest classifier achieved the best performance across all metrics (Accuracy: 0.70 +/- 0.14, Recall: 0.70 +/- 0.14, Precision: 0.70 +/- 0.16, F1-score: 0.67 +/- 0.15) at the window size of 9 seconds. CONCLUSIONS Wearable devices, powered with machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks, as it has a great potential to refrain people from touching their faces and potentially mitigate the possibility of transmitting COVID-19 and future respiratory diseases.

scholarly journals A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

mBio ◽  
2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Begüm D. Topçuoğlu ◽  
Nicholas A. Lesniak ◽  
Mack T. Ruffin ◽  
Jenna Wiens ◽  
Patrick D. Schloss
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Sequence Data ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Model Complexity ◽  
Support Vector ◽  
Classification Problems ◽  
Microbial Biomarkers

ABSTRACT Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability. IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

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