Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease)

Beunza, Juan-José; Puertas, Enrique; García-Ovejero, Ester; Villalba, Gema; Condés, Emilia; Koleva, Gergana; Hurtado, Cristian; Landecho, Manuel F.

doi:10.1016/j.jbi.2019.103257

Cited by 122 publications

(68 citation statements)

References 12 publications

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“…The reason for this was that the number of cases in our dataset was not big enough to sufficiently train the model (33). The multiple algorithms should be evaluated individually with each clinical dataset because a specific algorithm does not necessarily fit any dataset (34,35).…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

et al. 2020

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Section: Discussionmentioning

confidence: 99%

“…The hyperparameters of the machine-learning algorithms are set values which can greatly affect the performance of the prediction model (34)(35)(36). Thus, optimization of the hyperparameters is important for achieving the best prediction results from machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

et al. 2020

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“…4.1.2 Individual-level. This research thread focuses on the longitudinal predictive analysis of individual health-related events, including death occurrence [62], adverse drug events [185], sudden illnesses such as strokes [124] and cardiovascular events [23], as well as other clinical events [62] and life events [59] for different groups of people, including elders and people with mental disease. The goal here is usually to predict the time before an event occurs, although some researchers have attempted to predict the type of event.…”

Section: Healthcarementioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

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“…Each tree gives a classification, and the forest selects the classification having the most votes across all the trees in the forest. RF is also common to perform the prediction task in the medical domain [32,33]. The receiver operating characteristic (ROC) curve was constructed to assess the logistic regression and RF performance.…”

Section: Statistical Analysesmentioning

confidence: 99%

Dietary nutrients of relative importance associated with coronary artery disease: Public health implication from random forest analysis

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Dietary nutrients have significant effects on the risk of cardiovascular diseases. However, the results were not uniform across different countries. The study aims to determine the relative importance of dietary nutrients associated with coronary artery disease (CAD) among the Nepalese population. A hospital-based matched case-control study was carried out at Shahid Gangalal National Heart Center in Nepal. In the present study, patients with more than seventy percent stenosis in any main coronary artery branch in angiography were defined as cases, while those presenting normal coronary angiography or negative for stressed exercise test were considered controls. Dietary intakes of 612 respondents over the past 12 months were evaluated using a semi-quantitative customized food frequency questionnaire. In conditional regression model, the daily average dietary intake of β-carotene (OR: 0.54; 95%CI: 0.34, 0.87), and vitamin C (OR: 0.96; 95%CI: 0.93, 0.99) were inversely, whereas dietary carbohydrate (OR: 1.16; 95%CI: 1.1, 1.24), total fat/oil (OR: 1.47; 95%CI: 1.27, 1.69), saturated fatty acid (SFA) (OR: 1.2; 95%CI: 1.11, 1.3), cholesterol (OR: 1.01; 95%CI: 1.001, 1.014), and iron intakes (OR: 1.11; 95%CI: 1.03, 1.19) were positively linked with CAD. Moreover, in random forest analysis, the daily average dietary intakes of SFA, vitamin A, total fat/oil, β-carotene, and cholesterol were among the top five nutrients (out of 12 nutrients variables) of relative importance associated with CAD. The nutrients of relative importance imply a reasonable preventive measure in public health nutrients specific intervention to prevent CAD in a resource-poor country like Nepal. The findings are at best suggestive of a possible relationship between these nutrients and the development of CAD, but prospective cohort studies and randomized control trials will need to be performed in the Nepalese population.

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Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease)

Cited by 122 publications

References 12 publications

Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

Event Prediction in Big Data Era: A Systematic Survey

Dietary nutrients of relative importance associated with coronary artery disease: Public health implication from random forest analysis

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