Development and Deployment of a Machine Learning Model for Automatic Heart Failure Prediction

2021 ◽  
Author(s):  
K Arunaggiri Pandian ◽  
T S Sai Kumar ◽  
Sagar P. Dhandare ◽  
S Thabasum Aara
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Failure Prediction ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ahsan Huda ◽  
Adam Castaño ◽  
Anindita Niyogi ◽  
Jennifer Schumacher ◽  
Michelle Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
At Risk ◽  
Cardiac Amyloidosis ◽  
Learning Model ◽  
Wild Type ◽  
Derivation Cohort ◽  
Machine Learning Model ◽  
Patients At Risk ◽  
Amyloid Cardiomyopathy

AbstractTransthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.

scholarly journals Prediction for cardiac prognosis in patients with congestive heart failure by machine learning on dual-isotope myocardial semiconductor SPECT

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Shimizu ◽  
S Cho ◽  
K Hara ◽  
M Ohmori ◽  
R Tateishi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Random Forest ◽  
Roc Curve ◽  
Cardiac Death ◽  
Learning Model ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Dual Isotope ◽  
Machine Learning Model

Abstract Background Dual-isotope (low doze 201TlCl and 123I-β-methyl-P-iodophenyl-pentadecanoic acid (BMIPP)) single photon emission computed tomography (SPECT) is utilized to estimate myocardial damage in patients with congestive heart failure (CHF). However, predictive model construction on the SPECT for cardiac death by machine learning was not studied. Purpose To elucidate predictive value of machine learning model on dual-isotope SPECT for CHF. Methods We enrolled consecutive 310 patients who admitted with CHF (77.1±3.1 years, 164 males). After initial treatment, they underwent electrocardiography gated SPECT and observed in median 507 days [IQR: 165, 1032]. Multivariate Cox regression analysis for cardiac death was performed, and predictive model was constructed by ROC curve analysis and machine learning (Random Forest and Deep Learning). The accuracies (= [True positive + True negative] / Total) of the prediction models were compared with ROC curve model. Results Thirty-six patients fell into cardiac death. Cox analysis showed Age, left ventricular ejection fraction (LVEF), summed rest score (SRS) of BMIPP, and mismatch score were significant predictors (Hazard ratio: 1.068, 0.970, 1.032, 1.092, P value: <0.001, 0.014, 0.002, <0.001, respectively). ROC curve analysis of them revealed the accuracy of the cut-off value was 0.479–0.773. Conversely, machine learning model demonstrated higher accuracy for cardiac death (Random Forest: 0.895, Deep Learning: 0.935). The top 4 feature importance of the random forest were LVEF (0.299), SRS BMIPP (0.263), Age (0.262), and mismatch score (0.160). Conclusion Machine learning model on SPECT was superior to conventional statistic model for predicting cardiac death in patients with CHF. Funding Acknowledgement Type of funding source: None

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model
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