Improving prediction of one-year mortality of acute myocardial infarction using machine learning techniques

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Saad Zafar ◽  
Muhammad Zubair ◽  
Adeel Zafar ◽  
Najla Raza ◽  
Mirza Touseef
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
One Year

P1755Plasma proteomics in healthy individuals predict myocardial infarction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
R Hoogeveen ◽  
J P Belo Pereira ◽  
V Zampoleri ◽  
M J Bom ◽  
W Koenig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Plasma Proteins ◽  
Risk Model ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Traditional Risk Factors

Abstract Background Currently used models to predict cardiovascular event risk have limited value. It has been shown repetitively that the addition of single biomarkers has modest impact. Recently we observed that a model consisting of a larger array of plasma proteins performed very well in predicting the presence of vulnerable plaques in primary prevention patients. However, the validation of this protein panel in predicting cardiovascular outcomes remains to be established. Purpose This study investigated the ability of a 384 preselected protein biomarkers to predict acute myocardial infarction, using state-of-the-art machine learning techniques. Secondly, we compared the performance of this multi-protein risk model to traditional risk engines. Methods We selected 822 subjects from the EPIC-Norfolk prospective cohort study, of whom 411 suffered a myocardial infarction during follow-up (median 15 years) compared to 411 controls who remained event-free (median follow-up 20 years). The 384 proteins were measured using proximity extension assay technology. Machine learning algorithms (random forests) were used for the prediction of acute myocardial infarction (ICD code I21–22). Performance of the model was tested against and on top of traditional risk factors for cardiovascular disease (refit Framingham). All performance measurements were averaged over several stability selection routines. Results Prediction of myocardial infarction using a machine-learning model consisting of 50 plasma proteins resulted in a ROC AUC of 0.74±0.14, in comparison to 0.69±0.17 using traditional risk factors (refit Framingham. Combining the proteins and refit Framingham resulted in a ROC AUC of 0.74±0.15. Focussing on events occurring within 3 years after baseline blood withdrawal, the ROC AUC increased to 0.80±0.09 using 50 plasma proteins, as opposed to 0.67±0.22 using refit Framingham (figure). Combining the protein model with refit Framingham resulted in a ROC AUC of 0.82±0.11 for these events. Diagnostic performance events <3yrs Conclusion High-throughput proteomics outperforms traditional risk factors in prediction of acute myocardial infarction. Prediction of myocardial infarction occurring within 3 years after inclusion showed highest performance. Availability of affordable proteomic approaches and developed machine learning pave the path for clinical implementation of these models in cardiovascular risk prediction. Acknowledgement/Funding This study was funded by an ERA-CVD grant (JTC2017) and EU Horizon 2020 grant (REPROGRAM, 667837)

scholarly journals Myocardial Infarction Prediction Using Hybrid Machine Learning Techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 4251-4260
Author(s):  
Vaddi Niranjan Reddy Et.al
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Learning Classifier ◽  
Learning Techniques ◽  
Accuracy Performance ◽  
Graphical Presentation

The myocardial infarction prediction is an important task in health care domain in the current days. So, Prediction of cardiovascular diseases is a critical challenge in the area of clinical data analysis. It is difficult to predict myocardial infarction prediction by physicians with huge health records. To overcome this complexity we need to implement the automatic heard disease prediction system to notify the patient and get to recovery from the disease. Here to gaining the automatic system we are using machine learning techniques to easily performing myocardial infarction prediction. The machine learning techniques can be split into multiple types like unsupervised and supervised learning classifier. The supervised learning techniques working with structured data which is recommended to implement this classifiers. So, in this system we are using supervised learning techniques namely KNN, RF, NN, DT, NB, and SVM classifiers. To predict myocardial infarction, this system is using training dataset which is accessing from UCI ML repository. As well as this system is comparing accuracy performance between various machine learning algorithms and accuracy results with graphical presentation. This makes the accessing of the risk of the disease in the early stages and can try to save the patient without having any loss.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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