Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

Ben‐Assuli, Ofir; Heart, Tsipi; Klempfner, Robert; Padman, Rema

doi:10.1016/j.dss.2023.113982

Cited by 9 publications

(4 citation statements)

References 104 publications

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“…Data Cleaning [7], [13], [19], [20], [23], [26], [28], [32], [33], [34], [35], [36], [39], [40], [42], [41], [45], [46], [47], [54], [55], [60], [64], [65], [66], [68], [69] Mean imputation, predictive mean matching, median imputation, random forest imputation, kNN imputation, XGBoost imputation, and missForest…”

Section: Pre-processing Step Article Frequent Methodsmentioning

confidence: 99%

“…Data Transformation [13], [14], [22], [23], [24], [25], [26], [30], [31], [34], [36], [39], [41], [42], [43], [47], [54], [55], [58], [60], [62], [33], [63], [64], [65], [69] Recursive feature elminiation, SelectKBest, Chi-Square, Pearson's correlation, KS-Test, T-Test Data Reduction [47], [50], [54] PCA Data Balancing [13], [14], [21], [22], [23], [32], [33], [39], [43], [55], [62], [65], [66] SMOTE, Under-sampling, Oversampling, ADASYSN Data cleaning includes removing missing values from the dataset and performing imputation techniques to impute the missing values. The mean imputation frequently appears in various studies, while the machine learning-based imputation technique obtained better results than statistical-based imputation.…”

Section: Pre-processing Step Article Frequent Methodsmentioning

confidence: 99%

“…Involving expert cardiologists in the selection of features of the heart failure data collected from Sheba Medical Center, study [47] generated better performance in terms of a combination of accuracy, F1 score, recall, precision, and AUC (CI 95%) in predicting 30-day readmission of patients with heart failure. The study implemented three approaches to building their predictive models, categorized by the method of feature selection: machine-based, human expert-based, and collaboration-based between machine and human features.…”

Section: Readmissionmentioning

confidence: 99%

“…Demographic data such as age, gender, and race frequently appear in published studies and are used in their predictive analytics building. Study [47], which involved experts in selecting the features most related to heart failure risk prediction, reported that gender, age, and body mass index; expert-recommended demographical data; were the features most closely related to the risk of heart failure. For readmission prediction, [62] and [63] used a t-test approach and found age to be the leading feature for predicting 30-day heart failure readmission or death.…”

Section: Data Specification Used In Building Predictive Modelsmentioning

confidence: 99%

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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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