An Insight into Machine Learning Techniques for Predictive Analysis and Feature Selection

doi:10.35940/ijitee.i1055.0789s19

Cited by 15 publications

(2 citation statements)

References 17 publications

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“…In this work at first, after removing unrelated and redundant attributes through the expert consensus, 54 clinical factors that provide valuable prognostic information for death prediction were identified. The current study in addition shows it is essential to select the most important features to maximize the capability of the model when compared to the use of whole attributes from the dataset (34). These noteworthy topics rest on the precise feature selection of COVID-19 due to the high volume of data in COVID-19 databases.…”

Section: Discussionmentioning

confidence: 96%

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Shanbehzadeh¹,

Orooji²,

Kazemi-Arpanahi³

2021

jbe

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Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS)

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Section: Discussionmentioning

confidence: 96%

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Shanbehzadeh¹,

Orooji²,

Kazemi-Arpanahi³

2021

jbe

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“…The feature selection also determined the most optimal list of features and reduced the computational complexity of models. The GA as a feature selection method, which is based on the theory of natural selection or Darwin, can consider all possible connections between variables and identify the most proper combination of variables [30,31,34,37,38]. Therefore, GA iterations were implemented to select COVID-19 mortality predictors.…”

Section: Selection Of Feature Subsets With Gamentioning

confidence: 99%

Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms

2022

J Biomed Phys Eng

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Background: Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA).Objective: This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. Material and Methods:In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models.Results: A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. Conclusion:The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.

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Impact of predictor variables on the performance of future occupancy prediction: Feature selection using genetic algorithms and machine learning

Esrafilian-Najafabadi

Haghighat

2022

Building and Environment

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An Insight into Machine Learning Techniques for Predictive Analysis and Feature Selection

Cited by 15 publications

References 17 publications

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms

Impact of predictor variables on the performance of future occupancy prediction: Feature selection using genetic algorithms and machine learning

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