Optimizing cardiovascular disease mortality prediction: a super learner approach in the tehran lipid and glucose study

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Abstract

Background & aim

Cardiovascular disease (CVD) is the most important cause of death in the world and has a potential impact on health care costs, this study aimed to evaluate the performance of machine learning survival models and determine the optimum model for predicting CVD-related mortality.

Method

In this study, the research population was all participants in Tehran Lipid and Glucose Study (TLGS) aged over 30 years. We used the Gradient Boosting model (GBM), Support Vector Machine (SVM), Super Learner (SL), and Cox proportional hazard (Cox-PH) models to predict the CVD-related mortality using 26 features. The dataset was randomly divided into training (80%) and testing (20%). To evaluate the performance of the methods, we used the Brier Score (BS), Prediction Error (PE), Concordance Index (C-index), and time-dependent Area Under the Curve (TD-AUC) criteria. Four different clinical models were also performed to improve the performance of the methods.

Results

Out of 9258 participants with a mean age of (SD; range) 43.74 (15.51; 20–91), 56.60% were female. The CVD death proportion was 2.5% (228 participants). The death proportion was significantly higher in men (67.98% M, 32.02% F). Based on predefined selection criteria, the SL method has the best performance in predicting CVD-related mortality (TD-AUC > 93.50%). Among the machine learning (ML) methods, The SVM has the worst performance (TD-AUC = 90.13%). According to the relative effect, age, fasting blood sugar, systolic blood pressure, smoking, taking aspirin, diastolic blood pressure, Type 2 diabetes mellitus, hip circumference, body mss index (BMI), and triglyceride were identified as the most influential variables in predicting CVD-related mortality.

Conclusion

According to the results of our study, compared to the Cox-PH model, Machine Learning models showed promising and sometimes better performance in predicting CVD-related mortality. This finding is based on the analysis of a large and diverse urban population from Tehran, Iran.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-024-02489-0.

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Most cited references 47

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Assessing the performance of prediction models: a framework for traditional and novel measures.

Ewout W. Steyerberg, Andrew J Vickers, Nancy R Cook … (2010)

The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.

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Machine Learning in Medicine.

Rahul Deo (2015)

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.

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Is Open Access

Machine learning applications in cancer prognosis and prediction

Konstantina Kourou, Themis Exarchos, Konstantinos P Exarchos … (2014)

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

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Author and article information

Contributors

Safoora Gharibzadeh: sgh18@leicester.ac.uk

Leila Janani: l.janani@imperial.ac.uk

Journal

Journal ID (nlm-ta): BMC Med Inform Decis Mak

Journal ID (iso-abbrev): BMC Med Inform Decis Mak

Title: BMC Medical Informatics and Decision Making

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6947

Publication date (Electronic): 16 April 2024

Publication date PMC-release: 16 April 2024

Publication date Collection: 2024

Volume: 24

Electronic Location Identifier: 97

Affiliations

[1 ]Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, ( https://ror.org/03w04rv71) Tehran, Iran

[2 ]Department of Epidemiology and Biostatistics, Pasteur Institute of Iran, ( https://ror.org/00wqczk30) Tehran, Iran

[3 ]GRID grid.411600.2, Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, , Shahid Beheshti University of Medical Sciences, ; Tehran, Iran

[4 ]Department of Epidemiology and Biostatistics, School of Public health, Tehran University of Medical Sciences, ( https://ror.org/01c4pz451) Tehran, Iran

[5 ]Imperial Clinical Trials Unit, School of Public Health, Imperial College London, ( https://ror.org/041kmwe10) London, UK

Article

Publisher ID: 2489

DOI: 10.1186/s12911-024-02489-0

PMC ID: 11020797

PubMed ID: 38627734

SO-VID: 142cdaf2-8529-4aa8-af35-293ed0ce1f35

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 13 February 2023

Date accepted : 22 March 2024

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: machine learning,cox proportional hazard,gradient boosting model,support vector machine,super learner,tehran lipid and glucose study,cardiovascular disease

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: machine learning, cox proportional hazard, gradient boosting model, support vector machine, super learner, tehran lipid and glucose study, cardiovascular disease

Optimizing cardiovascular disease mortality prediction: a super learner approach in the tehran lipid and glucose study

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Abstract

Background & aim

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Conclusion

Supplementary Information

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Most cited references 47

Assessing the performance of prediction models: a framework for traditional and novel measures.

Machine Learning in Medicine.

Machine learning applications in cancer prognosis and prediction

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