scholarly journals A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260195
Author(s):  
Marcelo Dantas Tavares de Melo ◽  
Jose de Arimatéia Batista Araujo-Filho ◽  
José Raimundo Barbosa ◽  
Camila Rocon ◽  
Carlos Danilo Miranda Regis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ejection Fraction ◽  
Area Under The Curve ◽  
Random Forest Algorithm ◽  
Noncompaction Cardiomyopathy ◽  
2D Echocardiography ◽  
Specific Strain ◽  
Lv Ejection Fraction ◽  
Sensitivity Specificity

Aims Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based model. Methods and results Forty-nine outpatients with NCC diagnosis by echocardiography and magnetic resonance imaging (21 men, 42.8±14.8 years) were included. A comprehensive echocardiogram was performed. The layer-specific strain was analyzed from the apical two-, three, four-chamber views, short axis, and focused right ventricle views using 2D echocardiography (2DE) software. RBR was present in 44.9% of patients, and this group presented increased LV mass indexed (118±43.4 vs. 94.1±27.1g/m2, P = 0.034), LV end-diastolic and end-systolic volumes (P< 0.001), E/e’ (12.2±8.68 vs. 7.69±3.13, P = 0.034), and decreased LV ejection fraction (40.7±8.71 vs. 58.9±8.76%, P < 0.001) when compared to patients without RBR. Also, patients with RBR presented a significant decrease of global longitudinal, radial, and circumferential strain. When ML model based on a random forest algorithm and a neural network model was applied, it found that twist, NC/C, torsion, LV ejection fraction, and diastolic dysfunction are the strongest predictors to RBR with accuracy, sensitivity, specificity, area under the curve of 0.93, 0.99, 0.80, and 0.88, respectively. Conclusion In this study, a random forest algorithm was capable of selecting the best echocardiographic predictors to RBR pattern in NCC patients, which was consistent with worse systolic, diastolic, and myocardium deformation indices. Prospective studies are warranted to evaluate the role of this tool for NCC risk stratification.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

scholarly journals Random forest classification for predicting lifespan-extending chemical compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Forest ◽  
Molecular Descriptors ◽  
Area Under The Curve ◽  
Chemical Compounds ◽  
Research Area ◽  
Importance Measure ◽  
Random Forest Algorithm ◽  
Molecular Fingerprints ◽  
Age Related

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

Effects of air quality on the health of Mediterranean forests

2021 ◽  
Author(s):  
Adrián García Bruzón ◽  
Patricia Arrogante Funes ◽  
Laura Muñoz Moral
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Aridity Index ◽  
Plant Health ◽  
Mediterranean Forests ◽  
Random Forest Algorithm ◽  
The Mediterranean ◽  
Heterogeneous Variables ◽  
Peninsular Spain

&lt;p&gt;The climate change has turned out to be a determining factor in the development of forest in Spain. Production systems have emitted polluting gases and other particles into the atmosphere, for which some plants have not yet developed adaptation systems. Among the most harmful pollutants for the environment are gases such as nitrous oxides, ozone, particulate matter.&lt;/p&gt;&lt;p&gt;However, this condition is not the same in Peninsular Spain, and the Balearic Islands since the plant compositions differ in the territory and the bioclimatic, topographic, and anthropic characteristics. Monitoring the vegetation with sufficient spatial and temporal resolution, studying variables conditioning plant health is a challenge from the nature of the variables and the amount of data to be handled.&amp;#160;&lt;/p&gt;&lt;p&gt;The Mediterranean forest is one of the most ecosystem affected by climate change because of usually experimented long periods of drought that, in combination with increased temperatures, can drastically reduce the photosynthetic activity of trees and therefore the biomass of forests.&lt;/p&gt;&lt;p&gt;That is why the application of environmental technologies based on Remote Sensing (which provide plant health indices from passive sensors on satellite platforms and other variables of interest), Geographic Information Systems (to integrate, process, analyze spatial and temporal data) and machine learning models (which facilitate the extraction of relationships between variables, conditioning factors and predict patterns).&amp;#160;&lt;/p&gt;&lt;p&gt;In this regard, this work's objective is to evaluate the possible effect that different pollutants have on the health of the vegetation, measured from the annual values of the Normalized Difference Vegetation Index (NDVI), in the Mediterranean forests of Peninsular Spain. To achieve this, we are used machine learning techniques using the Random Forest algorithm. The study has also been done with various climatic, topographic, and anthropic variables that characterize the forest to carry it out.&amp;#160;&lt;/p&gt;&lt;p&gt;The results showed that certain variables such as the aridity index had generated the NDVI values and therefore plant development, while others are limiting factors such as the concentration of certain pollutants and the direct relationship between them particulates and NOx. This study can verify how the Random Forest algorithm offers reliable results, even when working with heterogeneous variables.&amp;#160;&lt;/p&gt;

scholarly journals Prediction of novel mouse TLR9 agonists using a random forest approach

2019 ◽  
Vol 20 (S2) ◽  
Author(s):  
Varun Khanna ◽  
Lei Li ◽  
Johnson Fung ◽  
Shoba Ranganathan ◽  
Nikolai Petrovsky
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Correlation Coefficient ◽  
Matthews Correlation Coefficient ◽  
Learning Algorithms ◽  
Ensemble Classifier ◽  
Innate Immune ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm

Abstract Background Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. Results Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including ‘CC’, ‘GG’,‘AG’, ‘CCCG’ and ‘CGGC’ were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. Conclusion We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.

scholarly journals Predicting mortality in hemodialysis patients using machine learning analysis

2020 ◽  
Author(s):  
Victoria Garcia-Montemayor ◽  
Alejandro Martin-Malo ◽  
Carlo Barbieri ◽  
Francesco Bellocchio ◽  
Sagrario Soriano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Prediction Models ◽  
Initial Period ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Prediction Of Mortality ◽  
Mortality Prediction Models

Abstract Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

scholarly journals Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches

2021 ◽  
Vol 18 (7) ◽  
pp. 3339
Author(s):  
Sunhae Kim ◽  
Hye-Kyung Lee ◽  
Kounseok Lee
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Suicidal Ideation ◽  
Random Forest ◽  
Suicide Ideation ◽  
Area Under The Curve ◽  
Learning Approaches ◽  
K Nearest Neighbors ◽  
Predictive Values ◽  
Linear Discriminant

(1) Background: The Patient Health Questionnaire-9 (PHQ-9) is a tool that screens patients for depression in primary care settings. In this study, we evaluated the efficacy of PHQ-9 in evaluating suicidal ideation (2) Methods: A total of 8760 completed questionnaires collected from college students were analyzed. The PHQ-9 was scored in combination with and evaluated against four categories (PHQ-2, PHQ-8, PHQ-9, and PHQ-10). Suicidal ideations were evaluated using the Mini-International Neuropsychiatric Interview suicidality module. Analyses used suicide ideation as the dependent variable, and machine learning (ML) algorithms, k-nearest neighbors, linear discriminant analysis (LDA), and random forest. (3) Results: Random forest application using the nine items of the PHQ-9 revealed an excellent area under the curve with a value of 0.841, with 94.3% accuracy. The positive and negative predictive values were 84.95% (95% CI = 76.03–91.52) and 95.54% (95% CI = 94.42–96.48), respectively. (4) Conclusion: This study confirmed that ML algorithms using PHQ-9 in the primary care field are reliably accurate in screening individuals with suicidal ideation.

scholarly journals Forest Fire Prediction using Machine Learning Models based on DC, Wind and RH

2020 ◽  
Vol 8 (6S) ◽  
pp. 142-143
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Forest Fire ◽  
Random Forest Algorithm ◽  
Learning Models ◽  
Learning Classifier ◽  
Machine Learning Models ◽  
Classifier Algorithms

The paper points out forest fire prediction using machine learning models on the basis of viz. DC, Wind, RH out of the several machine learning classifier algorithms, It is relevant that random forest algorithm generates optimum accuracy(99.61%).

scholarly journals A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia

2021 ◽  
Vol 8 ◽  
Author(s):  
Guan Wang ◽  
Yanbo Zhang ◽  
Sijin Li ◽  
Jun Zhang ◽  
Dongkui Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Random Forest ◽  
Prediction Model ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Machine Learning Algorithms ◽  
Brier Score ◽  
Random Forest Algorithm

Objective: Preeclampsia affects 2–8% of women and doubles the risk of cardiovascular disease in women after preeclampsia. This study aimed to develop a model based on machine learning to predict postpartum cardiovascular risk in preeclamptic women.Methods: Collecting demographic characteristics and clinical serum markers associated with preeclampsia during pregnancy of 907 preeclamptic women retrospectively, we predicted the cardiovascular risk (ischemic heart disease, ischemic cerebrovascular disease, peripheral vascular disease, chronic kidney disease, metabolic system disease or arterial hypertension). The study samples were divided into training sets and test sets randomly in the ratio of 8:2. The prediction model was developed by 5 different machine learning algorithms, including Random Forest. 10-fold cross-validation was performed on the training set, and the performance of the model was evaluated on the test set.Results: Cardiovascular disease risk occurred in 186 (20.5%) of these women. By weighing area under the curve (AUC), the Random Forest algorithm presented the best performance (AUC = 0.711[95%CI: 0.697–0.726]) and was adopted in the feature selection and the establishment of the prediction model. The most important variables in Random Forest algorithm included the systolic blood pressure, Urea nitrogen, neutrophil count, glucose, and D-Dimer. Random Forest algorithm was well calibrated (Brier score = 0.133) in the test group, and obtained the highest net benefit in the decision curve analysis.Conclusion: Based on the general situation of patients and clinical variables, a new machine learning algorithm was developed and verified for the individualized prediction of cardiovascular risk in post-preeclamptic women.

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