scholarly journals Machine Learning for Integrating Social Determinants in Cardiovascular Disease Prediction Models: A Systematic Review

2020 ◽  
Author(s):  
Yuan Zhao ◽  
Erica P Wood ◽  
Nicholas Mirin ◽  
Rajesh Vedanthan ◽  
Stephanie H Cook ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Cardiovascular Disease ◽  
Social Determinants ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Complex Nature ◽  
Disease Prediction ◽  
Causal Pathway

Background: Cardiovascular disease (CVD) is the number one cause of death worldwide, and CVD burden is increasing in low-resource settings and for lower socioeconomic groups worldwide. Machine learning (ML) algorithms are rapidly being developed and incorporated into clinical practice for CVD prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with addressing the social determinants of cardiovascular outcomes. We sought to review how social determinants of health (SDoH) and variables along their causal pathway are being included in ML algorithms in order to develop best practices for development of future machine learning algorithms that include social determinants. Methods: We conducted a systematic review using five databases (PubMed, Embase, Web of Science, IEEE Xplore and ACM Digital Library). We identified English language articles published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction, that incorporated SDoH and related variables. We included studies that used data from any source or study type. Studies were excluded if they did not include the use of any machine learning algorithm, were developed for non-humans, the outcomes were bio-markers, mediators, surgery or medication of CVD, rehabilitation or mental health outcomes after CVD or cost-effective analysis of CVD, the manuscript was non-English, or was a review or meta-analysis. We also excluded articles presented at conferences as abstracts and the full texts were not obtainable. The study was registered with PROSPERO (CRD42020175466). Findings: Of 2870 articles identified, 96 were eligible for inclusion. Most studies that compared ML and regression showed increased performance of ML, and most studies that compared performance with or without SDoH/related variables showed increased performance with them. The most frequently included SDoH variables were race/ethnicity, income, education and marital status. Studies were largely from North America, Europe and China, limiting the diversity of included populations and variance in social determinants. Interpretation: Findings show that machine learning models, as well as SDoH and related variables, improve CVD prediction model performance. The limited variety of sources and data in studies emphasize that there is opportunity to include more SDoH variables, especially environmental ones, that are known CVD risk factors in machine learning CVD prediction models. Given their flexibility, ML may provide opportunity to incorporate and model the complex nature of social determinants. Such data should be recorded in electronic databases to enable their use.

Comparison of Machine Learning Algorithms for Cardiovascular Disease Prediction

2021 ◽  
pp. 111-126
Author(s):  
Stuti Pandey ◽  
Abhay Kumar Agarwal
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbors ◽  
The University

Cardiovascular disease prediction is a research field of healthcare which depends on a large volume of data for making effective and accurate predictions. These predictions can be more effective and accurate when used with machine learning algorithms because it can disclose all the concealed facts which are helpful in making decisions. The processing capabilities of machine learning algorithms are also very fast which is almost infeasible for human beings. Therefore, the work presented in this research focuses on identifying the best machine learning algorithm by comparing their performances for predicting cardiovascular diseases in a reasonable time. The machine learning algorithms which have been used in the presented work are naïve Bayes, support vector machine, k-nearest neighbors, and random forest. The dataset which has been utilized for this comparison is taken from the University of California, Irvine (UCI) machine learning repository named “Heart Disease Data Set.”

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 Prediction of creep failure time using machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Soumyajyoti Biswas ◽  
David Fernandez Castellanos ◽  
Michael Zaiser
Keyword(s):  
Machine Learning ◽  
Creep Rate ◽  
Failure Time ◽  
Learning Algorithm ◽  
Acoustic Emissions ◽  
Creep Damage ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Residual Lifetime ◽  
Complex Nature

Abstract A subcritical load on a disordered material can induce creep damage. The creep rate in this case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady-state regime and a stage of accelerating creep that ultimately leads to catastrophic breakdown. Due to the statistical regularities in the creep rate, the time evolution of creep rate has often been used to predict residual lifetime until catastrophic breakdown. However, in disordered samples, these efforts met with limited success. Nevertheless, it is clear that as the failure is approached, the damage become increasingly spatially correlated, and the spatio-temporal patterns of acoustic emission, which serve as a proxy for damage accumulation activity, are likely to mirror such correlations. However, due to the high dimensionality of the data and the complex nature of the correlations it is not straightforward to identify the said correlations and thereby the precursory signals of failure. Here we use supervised machine learning to estimate the remaining time to failure of samples of disordered materials. The machine learning algorithm uses as input the temporal signal provided by a mesoscale elastoplastic model for the evolution of creep damage in disordered solids. Machine learning algorithms are well-suited for assessing the proximity to failure from the time series of the acoustic emissions of sheared samples. We show that materials are relatively more predictable for higher disorder while are relatively less predictable for larger system sizes. We find that machine learning predictions, in the vast majority of cases, perform substantially better than other prediction approaches proposed in the literature.

Performance Evaluation of Machine Learning Algorithms for Dengue Disease Prediction

2019 ◽  
Vol 16 (12) ◽  
pp. 5105-5110
Author(s):  
S. Kannimuthu ◽  
K. S. Bhuvaneshwari ◽  
D. Bhanu ◽  
A. Vaishnavi ◽  
S. Ahalya
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Severe Illness ◽  
Disease Prediction ◽  
Dengue Disease ◽  
Aedes Mosquitoes ◽  
Disease Prognosis ◽  
C4.5 Algorithm

Dengue is a dangerous disease caused by female mosquitoes. Dengue fever (also called as breakbone fever) is a infection that can cause to a severe illness which is happened by four different viruses and spread by Aedes mosquitoes. It is the necessary to devise effective methodology for dengue disease prognosis. Machine learning is a sub-filed of artificial intelligence (AI) which offers systems the ability to learn and improve from experience without human intervention and being explicitly programmed. In this research work, the performance analysis of various prediction models is done for dengue disease prediction. It is observed that C4.5 algorithm outperforms well in terms of performance measures such as accuracy (89.33%), prediction (88.9%), recall (89.77%) and other measures.

scholarly journals Validation and Generalizability of Machine Learning Prediction Models on Attrition in Longitudinal Studies

2021 ◽  
Author(s):  
Kristin Jankowsky ◽  
Ulrich Schroeders
Keyword(s):  
Machine Learning ◽  
Longitudinal Studies ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Missing At Random ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Unrealistic Assumption ◽  
Nationally Representative ◽  
Survey Attrition

Attrition in longitudinal studies is a major threat to the representativeness of the data and the generalizability of the findings. Typical approaches to address systematic nonresponse are either expensive and unsatisfactory (e.g., oversampling) or rely on the unrealistic assumption of data missing at random (e.g., multiple imputation). Thus, models that effectively predict who most likely drops out in subsequent occasions might offer the opportunity to take countermeasures (e.g., incentives). With the current study, we introduce a longitudinal model validation approach and examine whether attrition in two nationally representative longitudinal panel studies can be predicted accurately. We compare the performance of a basic logistic regression model to a more flexible, data-driven machine learning algorithm––Gradient Boosting Machines. Our results show almost no difference in accuracies for both modeling approaches, which contradicts claims of similar studies on survey attrition. Prediction models could not be generalized across surveys and were less accurate when tested at a later survey wave. We discuss the implications of these findings for survey retention, the use of complex machine learning algorithms, and give some recommendations to deal with study attrition.

scholarly journals The Learning Path to Neural Network Industrial Application in Distributed Environments

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2247
Author(s):  
Lenka Landryová ◽  
Jan Sikora ◽  
Renata Wagnerová
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Legacy Systems ◽  
Industrial Companies ◽  
Applied Machine Learning ◽  
Learning Path ◽  
Production Environments ◽  
Distributed Production

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data.

scholarly journals HEART DISEASE PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 1 (1) ◽  
pp. 146-176
Author(s):  
Israa Nadher ◽  
Mohammad Ayache ◽  
Hussein Kanaan
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Digital Data ◽  
Disease Prediction ◽  
Prediction System ◽  
Testing Dataset ◽  
Starting Point

Abstract—Information decision support systems are becomingmore in use as we are living in the era of digital data andrise of artificial intelligence. Heart disease as one of the mostknown and dangerous is getting very important attention, thisattention is translated into digital and prediction system thatdetects the presence of disease according to the available dataand information. Such systems faced a lot of problems since thefirst rise, but now with the deveolopment of machine learnigfield we are using them in developing new models to detect thepresence of this disease, in addition to algorithms data is veryimportant which also form the heart of the predicton systems,as we know prediction algorithms take decisions and thesedecisions must be based on facts, and these facts are extractedfrom data, as a result data is the starting point of every system.In this paper we propose a Heart Disease Prediction Systemusing Machine Learning Algorithms, in terms of data we usedCleveland dataset, this dataset is normalized then divided intothree scnearios in terms of traning and testing respectively,80%-20%, 50%-50%, 30%-70%. In each case of dataset ifit is normalized or not we will have these three scenarios.We used three machine learning algorithms for every scenarioof the mentioned before which are SVM, SMO and MLP, inthese algorithms we’ve used two different kernels to test theresults upon that. These two types of simulation are added tothe collection of scenarios mentioned above to become as thefollowing we have at the main level two types normalized andunnormalized dataset, then for each one we have three typesaccording to the amount of training and testing dataset, thenfor each of these scenarios we have two scenarios according tothe type of kernel to become 30 scenarios in total, our proposedsystem have shown a dominance in terms of accuracy over theother previous works.

scholarly journals Ensemble model for Heart Disease Prediction

2021 ◽  
Vol 1 (4) ◽  
pp. 268-280
Author(s):  
Bamanga Mahmud , , , Ahmad ◽  
Ahmadu Asabe Sandra ◽  
Musa Yusuf Malgwi ◽  
Dahiru I. Sajoh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Ensemble Model ◽  
Prediction Probability

For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. Heart is one of the essential parts of human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

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