A comparative study on prediction of survival event of heart failure patients using machine learning algorithms

Karakuş, Mücella Özbay; Er, Orhan

doi:10.1007/s00521-022-07201-9

Cited by 10 publications

(5 citation statements)

References 69 publications

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“…Because the dataset contains an imbalanced class, it used the SMOTE technique to address the imbalance problem. 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.…”

Section: Mortalitymentioning

confidence: 92%

“…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%

“…The Heart Failure dataset labels patients as either dead or alive, which allowed the proposed models to predict mortality. Like the above study, [54] used various machine learning algorithms to build mortality prediction models based on the UCI heart failure dataset. Unlike [53]'s approaches, this study used the PCA method to determine the effectiveness of dimension reduction in prediction performance.…”

Section: Mortalitymentioning

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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Section: Mortalitymentioning

confidence: 92%

Section: Pre-processing Step Article Frequent Methodsmentioning

confidence: 99%

Section: Pre-processing Step Article Frequent Methodsmentioning

confidence: 99%

Section: Mortalitymentioning

confidence: 99%

See 2 more Smart Citations

Predictive Analytics in Heart Failure Prediction: A Systematic Review

Hidayaturrohman,

Hanada

2024

Preprint

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“…As the number of samples available for learning increases, the algorithms adaptively improve their performance. ML is fundamentally about predicting the future based on past experience (12,13).…”

Section: Introductionmentioning

confidence: 99%

Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks

et al. 2023

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Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cell layers and the homogeneity of the appearance of the ooplasm are used to determine the quality of the oocytes to be used in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) methods. In this study, classification processes have been carried out using convolutional neural networks (CNN), a deep learning method, on the images of the cumulus-oocyte complex selected based on the theoretical knowledge and professional experience of embryologists. A convolutional neural network with a depth of 4 is used. In each depth level, one convolution, one ReLU and one max-pooling layer are included. The designed network architecture is trained using the Adam optimization algorithm. The cumulus-oocyte complexes (n=400) used in the study were obtained by using the oocyte aspiration method from the ovaries of the bovine slaughtered at the slaughterhouse. The CNN-based classification model developed in this study showed promising results in classifying three-class image data in terms of cumulus-oocyte complex classification. The classification model achieved high accuracy, precision, and sensitivity values on the test dataset. Continuous research and optimization of the model can further improve its performance and benefit the field of cumulus-oocyte complexes classification and oocyte quality assessment.

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Development of an Explainable Heart Failure Patients Survival Status Prediction Model Using Machine Learning Algorithms

Tsehay Demis,

Yibre

2024

Pan-African Conference on Artificial Intelligence

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A comparative study on prediction of survival event of heart failure patients using machine learning algorithms

Cited by 10 publications

References 69 publications

Predictive Analytics in Heart Failure Prediction: A Systematic Review

Predictive Analytics in Heart Failure Prediction: A Systematic Review

Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks

Development of an Explainable Heart Failure Patients Survival Status Prediction Model Using Machine Learning Algorithms

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