Mortality prediction of COVID-19 patients using soft voting classifier

Rai, Nishant; Kaushik, Naman; Kumar, Deepika; Raj, Chandan; Ali, Ahad

doi:10.1016/j.ijcce.2022.09.001

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…Additionally, a real-time open-source dataset was used to predict how this virus will grow in the future. The death rate of COVID-19 patients was predicted by Rai and colleagues [24] using majority rule-based ensemble approaches. In this work, feature selection, synthetic oversampling, and multivariate imputation were applied.…”

Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

Puttegowda,

DS,

Mallu

et al. 2024

TOPHJ

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Background The development of technology has a significant impact on every aspect of life, whether it is the medical industry or any other profession. The potential of artificial intelligence has been demonstrated in data processing and analysis is used to inform decisions in the field of health care. The most crucial action is the early detection of a life-threatening illness to stop its development and spread. Highly contagious COVID-19 is a disease that requires immediate attention as it has spread globally. There is a need for a technology that can be utilised to detect the virus because of how quickly it spreads. With the increased use of technology, we now have access to a wealth of COVID-19-related information that may be used to learn crucial details about the virus. Objective The objective of the work is to develop comprehensible machine learning models for the automatic prediction of COVID-19. These models aim to accurately predict the likelihood of COVID-19 infection based on relevant input features, such as symptoms, demographics, and potential diagnostic tests or imaging results. Methods In this work, we mainly aimed to assess how well different machine learning methods might predict COVID-19 situations. In order to do this, we thoroughly evaluated a variety of widely used classifiers in machine learning. Popular algorithms like the random forest, k-nearest neighbour, and logistic regression were all included in our analysis. Results To assess the performance of our suggested algorithms using different machine learning techniques, we used an open-source dataset in the study. Our algorithms performed better than other models that are currently in use, which is noteworthy. The high degree of precision in predicting COVID-19 instances is demonstrated by our remarkable accuracy of 96.34%. We also obtained a good F1 score of 0.98 for our models, indicating the strength and efficiency of our method in obtaining metrics for both recall and precision. Conclusion This work highlights the possibility of understanding machine learning algorithms for COVID-19 prediction automatically. We have shown that techniques such as logistic regression, random forest, and k-nearest neighbor methods may reliably predict COVID-19 situations while preserving interpretability. In order to promote acceptance and confidence among healthcare professionals and enable well-informed decision-making in clinical settings, these models' transparency is essential. To improve these models' efficacy and scalability in the future, more research will be needed to enhance and validate them on a variety of datasets. In the end, utilizing understandable machine learning algorithms presents encouraging opportunities for COVID-19 early diagnosis and control, supporting international public health campaigns.

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Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

Puttegowda,

DS,

Mallu

et al. 2024

TOPHJ

View full text Add to dashboard Cite

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“…In addition, the Lagrangian relaxation approach and a constructive heuristic algorithm are considered to overcome the problem convolution and to solve large-scale instances. More studies regarding recent finding on COVID-19 can be found in ( Rai et al, 2022 , Solayman et al, 2023 ). Table 2 illustrates the distinctions in various features considered for supply chain networks among the papers analyzed.…”

Section: Survey On Related Researchmentioning

confidence: 99%

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Moadab¹,

Kordi²,

Paydar³

et al. 2023

Expert Systems with Applications

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“…The final prediction of the ensemble is determined by a majority vote among the individual models [11]. On the other hand, soft voting considers the probabilities that each model assigns to each class label [12]. The final prediction is obtained by aggregating these probabilities, usually by summing or averaging them.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

2024

IJIES

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The evolution of technology and the internet has accelerated the pace of communication and information exchange. Despite technological advancements, the significant weakness lies in the persistent threat of cybercrime, manifesting in various forms like malware, phishing, and ransomware. To solve the cybercrime problems, this research aims to create an intrusion detection system model using a novel framework. In general, the proposed method consists of 3 stages: Data preprocessing, feature selection using ANOVA F-value combined with cross validation, and classification using weight-based voting classifier. Some machine learning methods used in the weight-based voting classifier are random forest, K-nearest neighbour, and logistic regression. The experiment results show that weight order and weight combination affect the detection performance. The proposed method produces an excellent precision value of 98.66%, higher than the single voting classifier.

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Mortality prediction of COVID-19 patients using soft voting classifier

Cited by 11 publications

References 46 publications

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

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