Machine Learning Techniques for Cardiovascular Risk Score -Prediction

2021 ◽  
Author(s):  
Jayasudha B S K ◽  
P N Sudha ◽  
Rachana S ◽  
Sahana. Anagha A Kashyap ◽  
Anusha L
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Risk Score ◽  
Machine Learning Techniques ◽  
Cardiovascular Risk Score ◽  
Learning Techniques

scholarly journals An Individualized Algorithm to Predict Mortality in COVID-19 Pneumonia: a Machine Learning Based Study

2022 ◽  
Author(s):  
Maria Elena Laino ◽  
Elena Generali ◽  
Tobia Tommasini ◽  
Giovanni Angelotti ◽  
Alessio Aghemo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Risk Score ◽  
Machine Learning Techniques ◽  
Clinical Settings ◽  
Risk Of Mortality ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Importance Analysis ◽  
High Level

IntroductionIdentifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow to analyze big amount of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.Material and methodsWe conducted a retrospective cohort study on hospitalized adults COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach on vital parameters, laboratory values, and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.Results1,135 consecutive patients (median age 70 years, 64% males) were enrolled, 48 patients were excluded, the cohort was randomly divided in training (760) and test (327). During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85, ±0.025), and three levels were defined that correlated well with in-hospital mortality.ConclusionsMachine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

scholarly journals Machine Learning techniques and Polygenic Risk Score application to prediction genetic diseases

2020 ◽  
Vol 9 (1) ◽  
pp. 5-14
Author(s):  
Nibeth Mena Mamani
Keyword(s):  
Machine Learning ◽  
Risk Score ◽  
Prediction Models ◽  
Genetic Diseases ◽  
Machine Learning Techniques ◽  
Polygenic Risk Score ◽  
Risk Prediction Models ◽  
Polygenic Risk ◽  
Learning Techniques ◽  
Being There

For the last 10 years and after important discoveries such as genomic understanding of the human being, there has been a considerable increase in the interest on research risk prediction models associated with genetic originated diseases through two principal approaches: Polygenic Risk Score and Machine Learning techniques. The aim of this work is the narrative review of the literature on Machine Learning techniques applied to obtaining the polygenic risk score, highlighting the most relevant research and applications at present. The application of these techniques has provided many benefits in the prediction of diseases, it is evident that the challenges of the use and optimization of these two approaches are still being discussed and investigated in order to have a greater precision in the prediction of genetic diseases.

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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