Survival prediction of heart failure patients using machine learning techniques

Newaz, Asif; Ahmed, Nadim; Haq, Farhan Shahriyar

doi:10.1016/j.imu.2021.100772

Cited by 35 publications

(7 citation statements)

References 23 publications

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“…By evaluating their accuracy, precision, recall, and F1 score, the proposed models resulted in better prediction performance than studies [53] and [54]. However, [56] claimed that the oversampling technique might cause a loss of information when addressing the imbalance problem. This study developed a robust Random Forest classifier that could handle imbalance problems in the UCI heart failure dataset.…”

Section: Mortalitymentioning

confidence: 96%

Predictive Analytics in Heart Failure Prediction: A Systematic Review

Hidayaturrohman,

Hanada

2024

Preprint

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Heart failure is a leading cause of death among people worldwide. The cost of treatment can be prohibitive, and early prediction of heart failure would reduce treatment costs to patients and hospitals. Improved prediction of readmission would also be of great help hospital, allowing them to better manage their treatment programs and budgets. This systematic review aims to summarize recent studies of predictive analytics models that have been constructed to predict heart failure and readmission. Some of these models use a statistical approach and others a machine learning approach. Electronic patient health records, including demography, physical and clinical values, and laboratory findings are used to design and build predictive models. Predictive analytics with good performance have become essential for clinicians and specialists. Choosing a suitable machine learning algorithm and data preprocessing technique will improve predictive model performance, including data imputation and class imbalance.

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Section: Mortalitymentioning

confidence: 96%

Predictive Analytics in Heart Failure Prediction: A Systematic Review

Hidayaturrohman,

Hanada

2024

Preprint

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“…Notably, when the entire set of attributes was employed for classification, the results for heart failure diagnosis exhibited excellent accuracy at 91.23 percent, sensitivity at 93.83 percent, and specificity at 89.62 percent. The findings of both studies contribute valuable insights to the field of heart failure prediction, with Newaz, Ahmed & Haq (2021) addressing data imbalance challenges through ensemble strategies and Plati et al (2021) emphasizing the significance of feature selection in improving diagnostic accuracy.…”

Section: Related Workmentioning

confidence: 98%

“… Newaz, Ahmed & Haq (2021) proposed a technique aimed at preventing heart failure in patients. The dataset employed in this research originated from the HF clinical record dataset collected from the Allied Hospital of Cardiology in Faisalabad, comprising 299 patient records.…”

Section: Related Workmentioning

confidence: 99%

Heart failure survival prediction using novel transfer learning based probabilistic features

Qadri,

Hashmi,

Raza

et al. 2024

PeerJ Computer Science

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Heart failure is a complex cardiovascular condition characterized by the heart’s inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

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“…Correlation analysis in survival models is essential for identifying relationships between variables impacting the time to an event, enhancing predictive accuracy [10], [11]. It aids in uncovering dependencies crucial for understanding how different factors influence survival outcomes [12], [17].…”

Section: 23: Correlation Analysis Of Key Factors Influencing Heart Fa...mentioning

confidence: 99%

Semi-Parametric and Accelerated Failure Time Survival Models for Heart Failure Prediction

Ragb,

Akhil,

Cheetakonduru

2024

Preprint

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Heart failure is a leading cause of death among Diabetic and Obese patients globally, contributing to 8.5% of all heart disease deaths and potentially 36% of cardiovascular disease deaths. Early detection is crucial for timely intervention, reducing symptoms, lowering hospitalizations, and improving patient outcomes through personalized management. This paper presents the use of Semi-Parametric and Accelerated Failure Time survival models (AFT) on Heart failure prediction. Cox Proportional Hazard model from the family of Semi-Parametric survival models and Weibull's Accelerated Failure Time models from the family of Accelerated Failure Time models have been compared and contrasted. The Cox PH model excels in its ability to adapt to various survival time distributions avoiding any assumptions on survival time distribution. The Cox proportional hazard allows for the examination of covariate effects on the hazard function, making it a widely used survival model. Weibull AFT model follows a parametric approach, directly estimating the distribution of the survival time. Medical records data of close to 299 patients, who had heart failure, collected during their clinical followup period is used for building and evaluating the model. The final evaluation of model performance was conducted, focusing on their capacity to predict the probability of patient survival beyond 250 days from the clinical visit. Among the Cox proportional hazard model and Weibull's AFT model, the Weibull's AFT model demonstrated superior performance compared to the Cox model. Remarkably, Weibull's model exhibited consistently exceptional performance in both train and test validations.

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Survival prediction of heart failure patients using machine learning techniques

Cited by 35 publications

References 23 publications

Predictive Analytics in Heart Failure Prediction: A Systematic Review

Predictive Analytics in Heart Failure Prediction: A Systematic Review

Heart failure survival prediction using novel transfer learning based probabilistic features

Semi-Parametric and Accelerated Failure Time Survival Models for Heart Failure Prediction

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