A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning

Devi, E. Anna; Athappan, V.; Devi, E. Ahila; Emayavaramban, G.; Sriragavi, S.; Sivaramkrishnan, M.

doi:10.1109/icears53579.2022.9752301

Cited by 6 publications

(1 citation statement)

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“…Koushik et al [11] and Devi et al [12], proposed a methodology to distinguish between positive and negative cases on the basis of symptoms using a public dataset provided by the Israeli Ministry of Health and a variety of machine-learning models with the best accuracy, precision, and recall, over 86%. In the second model, similar experiments were performed using the best model, obtaining 94% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Time Influence over Performance of Machine-Learning-Based Diagnosis: A Case Study of COVID-19 Pandemic in Brazil

Marques

Guedes

Abreu

2022

IJERPH

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Efficiently recognising severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) symptoms enables a quick and accurate diagnosis to be made, and helps in mitigating the spread of the coronavirus disease 2019. However, the emergence of new variants has caused constant changes in the symptoms associate with COVID-19. These constant changes directly impact the performance of machine-learning-based diagnose. In this context, considering the impact of these changes in symptoms over time is necessary for accurate diagnoses. Thus, in this study, we propose a machine-learning-based approach for diagnosing COVID-19 that considers the importance of time in model predictions. Our approach analyses the performance of XGBoost using two different time-based strategies for model training: month-to-month and accumulated strategies. The model was evaluated using known metrics: accuracy, precision, and recall. Furthermore, to explain the impact of feature changes on model prediction, feature importance was measured using the SHAP technique, an XAI technique. We obtained very interesting results: considering time when creating a COVID-19 diagnostic prediction model is advantageous.

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Section: Related Workmentioning

confidence: 99%