Gender based survival prediction models for heart failure patients: A case study in Pakistan

Zahid, Faisal Maqbool; Ramzan, Shakeela; Faisal, Shahla; Hussain, Ijaz

doi:10.1371/journal.pone.0210602

Cited by 40 publications

(17 citation statements)

References 28 publications

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“…The three features chosen by the QLattice when searching for symbolic forms were ejection fraction, serum creatinine , and age . This is consistent with other studies of the same data set using different feature selection methods[17, 5].…”

Section: Resultssupporting

confidence: 92%

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Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

Wilstrup¹,

Cave²

2021

Preprint

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Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone.We used a newly invented symbolic regression method called the QLat-tice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations.Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

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Section: Resultssupporting

confidence: 92%

“…In their study, Ahmad et al used a Cox model to predict survival of patients using much fewer covariates than the Seattle Heart Failure Model. The data set was made freely available by Ahmad et al, and it has subsequently been used in additional analyses using both survival models[17] and machine learning techniques[5].…”

Section: Introductionmentioning

confidence: 99%

Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

Wilstrup¹,

Cave²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The three features chosen by the QLattice when searching for symbolic forms were ejection fraction, serum creatinine, and age. This is consistent with other studies of the same data set using different feature selection methods [7,8].…”

Section: Feature Selectionsupporting

confidence: 92%

Combining Symbolic Regression with the Cox Proportional Hazards Model Improves Prediction of Heart Failure Deaths

Wilstrup¹,

Cave²

2021

Preprint

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Background: Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. Methods: We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.Results: An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. Conclusion: Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

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“…Together with their analysis description and results, Ahmad and coworkers made their dataset publicly available online ("Dataset" section), making it freely accessible to the scientific community [55]. Afterwards, Zahid and colleagues [56] analyzed the same dataset to elaborate two different sex-based mortality prediction models: one for men and one for women. Although the two aforementioned studies [52,56] presented interesting results, they tackled the problem by standard biostatistics methods, leaving room for machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Afterwards, Zahid and colleagues [56] analyzed the same dataset to elaborate two different sex-based mortality prediction models: one for men and one for women. Although the two aforementioned studies [52,56] presented interesting results, they tackled the problem by standard biostatistics methods, leaving room for machine learning approaches. We aim here to fill this gap by using several data mining techniques first to predict survival of the patients, and then to rank the most important features included in the medical records.…”

Section: Introductionmentioning

confidence: 99%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

400

171

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Background: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. Methods: In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results: Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. Conclusions: This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

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Gender based survival prediction models for heart failure patients: A case study in Pakistan

Cited by 40 publications

References 28 publications

Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

Combining Symbolic Regression with the Cox Proportional Hazards Model Improves Prediction of Heart Failure Deaths

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

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