Sunspot Classifications & Solar Flare Prediction: Does machine learning improve upon Poisson-based prediction models?

2021 ◽  
Author(s):  
Aoife McCloskey ◽  
Shaun Bloomfield ◽  
Peter Gallagher
Keyword(s):  
Machine Learning ◽  
Solar Cycle ◽  
Solar Flares ◽  
Cross Validation ◽  
Prediction Models ◽  
Skill Scores ◽  
Using Data ◽  
The Individual ◽  
Multiple Metrics ◽  
Cycle Dependence

<p>Historically, McIntosh classifications of sunspots have been utilised for the prediction of solar flares, with modern day operational flare forecast services still reliant upon these classifications for their predictions. Here, building upon previous Poisson-based flare forecasting models that make use of Mcintosh classifications, a set of various machine learning (ML) techniques are applied to construct a set of new models to predict flares within a 24-hr period.</p><p>These ML algorithms are trained and tested using data from a range of independent solar cycle periods, cross-validation techniques are applied and the relative performance of each algorithm is compared. In order to make a direct comparison to Poisson-based forecasts, skill scores are calculated and the performance of each model is presented, results showing that the ML models perform well across multiple metrics. The implications these results have when compared with the previous Poisson-based approach are discussed as well as the problem of solar cycle dependence. Additionally, an exploration of the importance of the individual features (i.e., McIntosh components) on the performance of each prediction model and their physical implications are presented.</p>

scholarly journals Predicting Solar Flares with Machine Learning: Investigating Solar Cycle Dependence

2020 ◽  
Vol 895 (1) ◽  
pp. 3
Author(s):  
Xiantong Wang ◽  
Yang Chen ◽  
Gabor Toth ◽  
Ward B. Manchester ◽  
Tamas I. Gombosi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solar Cycle ◽  
Solar Flares ◽  
Cycle Dependence

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fathima Aliyar Vellameeran ◽  
Thomas Brindha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Research Papers ◽  
Using Data ◽  
Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

scholarly journals Machine learning-based clinical outcome prediction in surgery for acromegaly

Endocrine ◽  
2021 ◽  
Author(s):  
Olivier Zanier ◽  
Matteo Zoli ◽  
Victor E. Staartjes ◽  
Federica Guaraldi ◽  
Sofia Asioli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transsphenoidal Surgery ◽  
Prediction Models ◽  
External Validation ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Total Resection ◽  
Prior Surgery ◽  
Using Data ◽  
Csf Leaks

Abstract Purpose Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly. Methods Using data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed. Results The derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59–0.88) for GTR, 0.63 (0.40–0.82) for BR, as well as 0.77 (0.62–0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively. Conclusions Gross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization.

scholarly journals Covid-19 Analysis and Prediction using Data Science and Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 303-307
Author(s):  
Akshata Kulkarni
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Information Dissemination ◽  
Prediction Models ◽  
Learning Model ◽  
Future Trend ◽  
Control Measures ◽  
Machine Learning Model ◽  
Using Data ◽  
Novel Coronavirus

Abstract: Officials around the world are using several COVID-19 outbreak prediction models to make educated decisions and enact necessary control measures. In this study, we developed a Machine Learning model which predicts and forecasts the COVID-19 outbreak in India, with the goal of determining the best regression model for an in-depth examination of the novel coronavirus. Based on data available from January 31 to October 31, 2020, collected from Kaggle, this model predicts the number of confirmed cases in Maharashtra. We're using a Machine Learning model to foresee the future trend of these situations. The project has the potential to demonstrate the importance of information dissemination in improving response time and planning ahead of time to help reduce risk.

scholarly journals Machine-Learning Prediction of Comorbid Substance Use Disorders in ADHD Youth Using Swedish Registry Data

2019 ◽  
Author(s):  
Yanli Zhang-James ◽  
Qi Chen ◽  
Ralf Kuja-Halkola ◽  
Paul Lichtenstein ◽  
Henrik Larsson ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Registry Data ◽  
Longitudinal Models ◽  
Cross Sectional ◽  
Using Data

AbstractBackgroundChildren with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.MethodsPsychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989-1993. We trained 1) cross-sectional machine learning models using data available by age 17 to predict SUD diagnosis between ages 18-19; and 2) a longitudinal model to predict new diagnoses at each age.ResultsThe area under the receiver operating characteristic curve (AUC) was 0.73 and 0.71 for the random forest and multilayer perceptron cross-sectional models. A prior diagnosis of SUD was the most important predictor, accounting for 25% of correct predictions. However, after excluding this predictor, our model still significantly predicted the first-time diagnosis of SUD during age 18-19 with an AUC of 0.67. The average of the AUCs from longitudinal models predicting new diagnoses one, two, five and ten years in the future was 0.63.ConclusionsSignificant predictions of at-risk co-morbid SUDs in individuals with ADHD can be achieved using population registry data, even many years prior to the first diagnosis. Longitudinal models can potentially monitor their risks over time. More work is needed to create prediction models based on electronic health records or linked population-registers that are sufficiently accurate for use in the clinic.

scholarly journals In silico prediction of chemical neurotoxicity using machine learning

2020 ◽  
Vol 9 (3) ◽  
pp. 164-172
Author(s):  
Changsheng Jiang ◽  
Piaopiao Zhao ◽  
Weihua Li ◽  
Yun Tang ◽  
Guixia Liu
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Cross Validation ◽  
Prediction Models ◽  
Drug Withdrawal ◽  
Molecular Descriptors ◽  
Computational Prediction ◽  
Machine Learning Algorithms ◽  
Training Set ◽  
Data Set

Abstract Neurotoxicity is one of the main causes of drug withdrawal, and the biological experimental methods of detecting neurotoxic toxicity are time-consuming and laborious. In addition, the existing computational prediction models of neurotoxicity still have some shortcomings. In response to these shortcomings, we collected a large number of data set of neurotoxicity and used PyBioMed molecular descriptors and eight machine learning algorithms to construct regression prediction models of chemical neurotoxicity. Through the cross-validation and test set validation of the models, it was found that the extra-trees regressor model had the best predictive effect on neurotoxicity (${q}_{\mathrm{test}}^2$ = 0.784). In addition, we get the applicability domain of the models by calculating the standard deviation distance and the lever distance of the training set. We also found that some molecular descriptors are closely related to neurotoxicity by calculating the contribution of the molecular descriptors to the models. Considering the accuracy of the regression models, we recommend using the extra-trees regressor model to predict the chemical autonomic neurotoxicity.

scholarly journals Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

2020 ◽  
Author(s):  
Jaime Lynn Speiser ◽  
Kathryn E Callahan ◽  
Denise K Houston ◽  
Jason Fanning ◽  
Thomas M Gill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Models ◽  
Learning Methods ◽  
Life Study ◽  
Fall Injury ◽  
Machine Learning Methods ◽  
Using Data

Abstract Background Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty in understanding the complex algorithms that underlie models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. Method We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Results Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated using data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Conclusions Machine learning methods offer an alternative to traditional approaches for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

scholarly journals Designing risk prediction models for ambulatory no-shows across different specialties and clinics

2018 ◽  
Vol 25 (8) ◽  
pp. 924-930 ◽  
Author(s):  
Xiruo Ding ◽  
Ziad F Gellad ◽  
Chad Mather ◽  
Pamela Barth ◽  
Eric G Poon ◽  
...  
Keyword(s):  
Risk Score ◽  
Prediction Models ◽  
Clinic Visit ◽  
Risk Scores ◽  
Electronic Health Record Data ◽  
Logistic Regression Models ◽  
C Statistic ◽  
Using Data ◽  
The Individual ◽  
Outpatient Appointments

Abstract Objective As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.

scholarly journals Machine Learning Prediction of Stroke Mechanism in Embolic Strokes of Undetermined Source

Stroke ◽  
2020 ◽  
Vol 51 (9) ◽  
Author(s):  
Hooman Kamel ◽  
Babak B. Navi ◽  
Neal S. Parikh ◽  
Alexander E. Merkler ◽  
Peter M. Okin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Random Search ◽  
Model Performance ◽  
Area Under The Curve ◽  
Independent Set ◽  
Predicted Probability ◽  
Using Data

Background and Purpose: One-fifth of ischemic strokes are embolic strokes of undetermined source (ESUS). Their theoretical causes can be classified as cardioembolic versus noncardioembolic. This distinction has important implications, but the categories’ proportions are unknown. Methods: Using data from the Cornell Acute Stroke Academic Registry, we trained a machine-learning algorithm to distinguish cardioembolic versus non-cardioembolic strokes, then applied the algorithm to ESUS cases to determine the predicted proportion with an occult cardioembolic source. A panel of neurologists adjudicated stroke etiologies using standard criteria. We trained a machine learning classifier using data on demographics, comorbidities, vitals, laboratory results, and echocardiograms. An ensemble predictive method including L1 regularization, gradient-boosted decision tree ensemble (XGBoost), random forests, and multivariate adaptive splines was used. Random search and cross-validation were used to tune hyperparameters. Model performance was assessed using cross-validation among cases of known etiology. We applied the final algorithm to an independent set of ESUS cases to determine the predicted mechanism (cardioembolic or not). To assess our classifier’s validity, we correlated the predicted probability of a cardioembolic source with the eventual post-ESUS diagnosis of atrial fibrillation. Results: Among 1083 strokes with known etiologies, our classifier distinguished cardioembolic versus noncardioembolic cases with excellent accuracy (area under the curve, 0.85). Applied to 580 ESUS cases, the classifier predicted that 44% (95% credibility interval, 39%–49%) resulted from cardiac embolism. Individual ESUS patients’ predicted likelihood of cardiac embolism was associated with eventual atrial fibrillation detection (OR per 10% increase, 1.27 [95% CI, 1.03–1.57]; c-statistic, 0.68 [95% CI, 0.58–0.78]). ESUS patients with high predicted probability of cardiac embolism were older and had more coronary and peripheral vascular disease, lower ejection fractions, larger left atria, lower blood pressures, and higher creatinine levels. Conclusions: A machine learning estimator that distinguished known cardioembolic versus noncardioembolic strokes indirectly estimated that 44% of ESUS cases were cardioembolic.

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