scholarly journals Machine Learning Models for COVID-19 Detection in Brazil Based on Symptoms (Preprint)

2021 ◽  
Author(s):  
Íris Viana dos Santos Santana ◽  
Andressa C. M. da Silveira ◽  
Álvaro Sobrinho ◽  
Lenardo Chaves e Silva ◽  
Leandro Dias da Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Area Under The Curve ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Accuracy Score ◽  
K Nearest Neighbors ◽  
Runny Nose ◽  
Extreme Gradient Boosting

BACKGROUND controlling the COVID-19 outbreak in Brazil is considered a challenge of continental proportions due to the high population and urban density, weak implementation and maintenance of social distancing strategies, and limited testing capabilities. OBJECTIVE to contribute to addressing such a challenge, we present the implementation and evaluation of supervised Machine Learning (ML) models to assist the COVID-19 detection in Brazil based on early-stage symptoms. METHODS firstly, we conducted data preprocessing and applied the Chi-squared test in a Brazilian dataset, mainly composed of early-stage symptoms, to perform statistical analyses. Afterward, we implemented ML models using the Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. We evaluated the ML models using precision, accuracy score, recall, the area under the curve, and the Friedman and Nemenyi tests. Based on the comparison, we grouped the top five ML models and measured feature importance. RESULTS the MLP model presented the highest mean accuracy score, with more than 97.85%, when compared to GBM (> 97.39%), RF (> 97.36%), DT (> 97.07%), XGBoost (> 97.06%), KNN (> 95.14%), and SVM (> 94.27%). Based on the statistical comparison, we grouped MLP, GBM, DT, RF, and XGBoost, as the top five ML models, because the evaluation results are statistically indistinguishable. The ML models` importance of features used during predictions varies from gender, profession, fever, sore throat, dyspnea, olfactory disorder, cough, runny nose, taste disorder, and headache. CONCLUSIONS supervised ML models effectively assist the decision making in medical diagnosis and public administration (e.g., testing strategies), based on early-stage symptoms that do not require advanced and expensive exams.

Machine learning methods for predicting postpartum depression: A scoping review (Preprint)

2021 ◽  
Author(s):  
Kiran Saqib ◽  
Amber Fozia Khan ◽  
Zahid Ahmad Butt
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Postpartum Depression ◽  
Scoping Review ◽  
Early Stage ◽  
Maternal Mental Health ◽  
Gradient Boosting ◽  
Support Vector ◽  
Study Results ◽  
Extreme Gradient Boosting

BACKGROUND Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely given the rapid technological developments in recent years. OBJECTIVE This paper aims to synthesize the literature on machine learning and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). METHODS A scoping review methodology using the Arksey and O’Malley framework was employed to rapidly map the research activity in the field of ML for predicting PPD. A literature search was conducted through health and IT research databases, including PsycInfo, PubMed, IEEE Xplore and the ACM Digital Library from Sep 2020 till Jan 2021. Data were extracted on the article’s ML model, data type, and study results. RESULTS A total of fourteen (14) studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine (SVM) and random forests (RF) were the most commonly employed algorithms in addition to naïve Bayes, regression, artificial neural network, decision trees and extreme gradient boosting. There was considerable heterogeneity in the best performing ML algorithm across selected studies. The area under the receiver-operating-characteristic curve (AUC) values reported for different algorithms were SVM (Range: 0.78-0.86); RF method (0.88); extreme gradient boosting (0.80); logistic regression (0.93); and extreme gradient boosting (0.71) respectively. CONCLUSIONS ML algorithms are capable of analyzing larger datasets and performing more advanced computations, that can significantly improve the detection of PPD at an early stage. Further clinical-research collaborations are required to fine-tune ML algorithms for prediction and treatments. ML might become part of evidence-based practice, in addition to clinical knowledge and existing research evidence.

Machine learning methods for predicting postpartum depression: A scoping review (Preprint)

2021 ◽  
Author(s):  
Kiran Saqib ◽  
Amber Fozia Khan ◽  
Zahid Ahmad Butt
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Postpartum Depression ◽  
Scoping Review ◽  
Early Stage ◽  
Maternal Mental Health ◽  
Gradient Boosting ◽  
Support Vector ◽  
Study Results ◽  
Extreme Gradient Boosting

BACKGROUND Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely given the rapid technological developments in recent years. OBJECTIVE This paper aims to synthesize the literature on machine learning and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). METHODS A scoping review methodology using the Arksey and O’Malley framework was employed to rapidly map the research activity in the field of ML for predicting PPD. Two independent researchers searched PsycInfo, PubMed, IEEE Xplore and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted on the article’s ML model, data type, and study results. RESULTS A total of fourteen (14) studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine (SVM) and random forests (RF) were the most commonly employed algorithms in addition to naïve Bayes, regression, artificial neural network, decision trees and extreme gradient boosting. There was considerable heterogeneity in the best performing ML algorithm across selected studies. The area under the receiver-operating-characteristic curve (AUC) values reported for different algorithms were SVM (Range: 0.78-0.86); RF method (0.88); extreme gradient boosting (0.80); logistic regression (0.93); and extreme gradient boosting (0.71) respectively. CONCLUSIONS ML algorithms are capable of analyzing larger datasets and performing more advanced computations, that can significantly improve the detection of PPD at an early stage. Further clinical-research collaborations are required to fine-tune ML algorithms for prediction and treatments. ML might become part of evidence-based practice, in addition to clinical knowledge and existing research evidence.

scholarly journals Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys

Foods ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1543
Author(s):  
Fernando Mateo ◽  
Andrea Tarazona ◽  
Eva María Mateo
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Pollen Grains ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Test Set ◽  
Extreme Gradient Boosting ◽  
Classical Methodology

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst.

scholarly journals Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-20
Author(s):  
Nihad Brahimi ◽  
Huaping Zhang ◽  
Lin Dai ◽  
Jianzi Zhang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Research Work ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Car Sharing ◽  
K Nearest Neighbors ◽  
Extreme Gradient Boosting ◽  
On The Road

The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time

scholarly journals iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1454
Author(s):  
Phasit Charoenkwan ◽  
Watshara Shoombuatong ◽  
Chalaithorn Nantasupha ◽  
Tanarat Muangmool ◽  
Prapaporn Suprasert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Early Stage ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Efficient System ◽  
Extreme Gradient Boosting ◽  
Independent Test ◽  
Early Stage Cervical Cancer

Radical hysterectomy is a recommended treatment for early-stage cervical cancer. However, the procedure is associated with significant morbidities resulting from the removal of the parametrium. Parametrial cancer invasion (PMI) is found in a minority of patients but the efficient system used to predict it is lacking. In this study, we develop a novel machine learning (ML)-based predictive model based on a random forest model (called iPMI) for the practical identification of PMI in women. Data of 1112 stage IA-IIA cervical cancer patients who underwent primary surgery were collected and considered as the training dataset, while data from an independent cohort of 116 consecutive patients were used as the independent test dataset. Based on these datasets, iPMI-Econ was then developed by using basic clinicopathological data available prior to surgery, while iPMI-Power was also introduced by adding pelvic node metastasis and uterine corpus invasion to the iPMI-Econ. Both 10-fold cross-validations and independent test results showed that iPMI-Power outperformed other well-known ML classifiers (e.g., logistic regression, decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes, support vector machine, and extreme gradient boosting). Upon comparison, it was found that iPMI-Power was effective and had a superior performance to other well-known ML classifiers in predicting PMI. It is anticipated that the proposed iPMI may serve as a cost-effective and rapid approach to guide important clinical decision-making.

scholarly journals An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging

2020 ◽  
Vol 9 (3) ◽  
pp. 658 ◽  
Author(s):  
Jun-Cheng Weng ◽  
Tung-Yeh Lin ◽  
Yuan-Hsiung Tsai ◽  
Man Teng Cheok ◽  
Yi-Peng Eve Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting ◽  
Depressive Patients

It is estimated that at least one million people die by suicide every year, showing the importance of suicide prevention and detection. In this study, an autoencoder and machine learning model was employed to predict people with suicidal ideation based on their structural brain imaging. The subjects in our generalized q-sampling imaging (GQI) dataset consisted of three groups: 41 depressive patients with suicidal ideation (SI), 54 depressive patients without suicidal thoughts (NS), and 58 healthy controls (HC). In the GQI dataset, indices of generalized fractional anisotropy (GFA), isotropic values of the orientation distribution function (ISO), and normalized quantitative anisotropy (NQA) were separately trained in different machine learning models. A convolutional neural network (CNN)-based autoencoder model, the supervised machine learning algorithm extreme gradient boosting (XGB), and logistic regression (LR) were used to discriminate SI subjects from NS and HC subjects. After five-fold cross validation, separate data were tested to obtain the accuracy, sensitivity, specificity, and area under the curve of each result. Our results showed that the best pattern of structure across multiple brain locations can classify suicidal ideates from NS and HC with a prediction accuracy of 85%, a specificity of 100% and a sensitivity of 75%. The algorithms developed here might provide an objective tool to help identify suicidal ideation risk among depressed patients alongside clinical assessment.

scholarly journals Importance of GWAS risk loci and clinical data in predicting asthma using machine-learning approaches

2020 ◽  
Author(s):  
Si-Qiao Liang ◽  
Jian-Xiong Long ◽  
Jingmin Deng ◽  
Xuan Wei ◽  
Mei-Ling Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Clinical Data ◽  
Genome Wide Association Study ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Extreme Gradient Boosting

Abstract Asthma is a serious immune-mediated respiratory airway disease. Its pathological processes involve genetics and the environment, but it remains unclear. To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches. A case–control study with 123 asthma patients and 100 healthy controls was conducted in Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches (e.g., extreme gradient boosting [XGBoost], decision tree, support vector machine, and random forest algorithms) were used to identify the major factors that contributed to asthma. A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times of 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances were area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found that the top six risk factors for predicting asthma were rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index. Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma and thus provide insights into the disease pathogenesis of asthma. Further research is required to evaluate more genetic markers and clinical data and predict asthma risk.

scholarly journals Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Moojung Kim ◽  
Young Jae Kim ◽  
Sung Jin Park ◽  
Kwang Gi Kim ◽  
Pyung Chun Oh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Influenza Vaccination ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Age Group ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Abstract Background Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination Methods Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups. Results The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). Conclusions The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.

scholarly journals Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arturo Moncada-Torres ◽  
Marissa C. van Maaren ◽  
Mathijs P. Hendriks ◽  
Sabine Siesling ◽  
Gijs Geleijnse
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Explicit Knowledge ◽  
Cox Regression ◽  
Metastatic Breast ◽  
Gradient Boosting ◽  
Support Vector ◽  
Netherlands Cancer Registry ◽  
Extreme Gradient Boosting ◽  
The Impact

AbstractCox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ($$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ($$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

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