Development of an Explainable Prediction Model of Heart Failure Survival by Using Ensemble Trees

Moreno-Sánchez, Pedro A.

doi:10.1109/bigdata50022.2020.9378460

Cited by 31 publications

(15 citation statements)

References 47 publications

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“…A deeper analysis of existing survival predictors reveals that among the 74 studies 54 utilized publicly accessible data from three key databases: the Cancer Genome Atlas Program (TCGA) 17 , NCI Genomic Data Commons (GDC) 18 , and the Gene Expression Omnibus (GEO) 31, 32, 72, 73, 80, 82, 87, 90, 91, 130, 131 . Apart from public databases, there also exist private databases that have been utilized in existing survival prediction studies 66,75,81,112,113,117,118 . However, these private databases often restrict data access and may require extensive research proposals for data retrieval.…”

Section: Resultsmentioning

confidence: 99%

“…Table 6 illustrates 26 different feature engineering methods that have been utilized in diverse survival prediction studies. These methods are broadly categorized into five categories, namely supervised methods, incorporating L1 regularized Cox regression 29 , RSF algorithm 29 , Cox regression 103 , least absolute shrinkage and selection operator (lasso) regression 120 , cascaded Wx 105 , recursive feature elimination 38 , Boruta 31 , Akaike information criterion (AIC) regression 114 , variance 72 , lasso analysis 40 , multivariate regression 40 , Chi-squared 118 , mutual information 118 , and ANOVA 39,118 . Additionally, Network based methods include network based stratification (NBS) 83 , weighted correlation network analysis (WGCNA) 86 , canonical correlation analyses (CCA) 67 , patient similarity networks 38 , and neighborhood component analysis (NCA) 23 .…”

Section: Resultsmentioning

confidence: 99%

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Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

Abbasi,

Asim,

Ahmed

et al. 2024

Preprint

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Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, the need of the hour is to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 22 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 74 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

Abbasi,

Asim,

Ahmed

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This transparency helps funding agencies, oversight boards, and executive teams explain their decisions about funding and governing decisions as well as the system's operation. In the healthcare domain, lack of explainability limits a wider adoption of AI solutions since healthcare workers often find it challenging to trust complex models since they require high technical and statistics knowledge (Moreno-Sanchez, 2020).…”

Section: Discussionmentioning

confidence: 99%

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

et al. 2021

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This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.

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“…Recently, [21] developed a heart failure survival prediction model with the help of an ensemble tree machine learning approach. Extreme Gradient Boosting (XGBoost) was demonstrated as the most accurate classifier with 83.00%.…”

Section: A Filter Methodsmentioning

confidence: 99%

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Zaini¹,

Awang²

2023

IJACSA

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In cardiology, as in other medical specialties, early and accurate diagnosis of heart disease is crucial as it has been the leading cause of death over the past few decades. Early prediction of heart disease is now more crucial than ever. However, the state-of-the-art heart disease prediction strategy put more emphasis on classifier selection in enhancing the accuracy and performance of heart disease prediction, and seldom considers feature reduction techniques. Furthermore, there are several factors that lead to heart disease, and it is critical to identify the most significant characteristics in order to achieve the best prediction accuracy and increase prediction performance. Feature reduction reduces the dimensionality of the information, which may allow learning algorithms to work quicker and more efficiently, producing predictive models with the best rate of accuracy. In this study, we explored and suggested a hybrid of two distinct feature reduction techniques, chi-squared and analysis of variance (ANOVA). In addition, using the ensemble stacking method, classification is performed on selected features to classify the data. Using the optimal features based on hybrid features combination, the performance of a stacking ensemble based on logistic regression yields the best result with 93.44%. This can be summarized as the feature selection method can take into account as an effective method for the prediction of heart disease.

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Development of an Explainable Prediction Model of Heart Failure Survival by Using Ensemble Trees

Cited by 31 publications

References 47 publications

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

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